Dr. Kelvin Choi: Good morning, everyone.
This is Kelvin Choi, senior investigator
from the National Institute of Minority Health
and Health Disparities. Welcome to the workshop.
This is the first NIH Health Disparities
Interest Group workshop titled Integrating Social Determinants
and Structural Influences Measures in Biomedical Research.
We have overwhelming responses from you all,
and we have a lot of people register.
So I'm glad that even though it's very early
on the West Coast, you're able to join us,
and for the East Coast people, I'm glad that you have time
to get a cup of coffee before we start.
Before I go further into the workshop,
let me also thank the workshop
planning committee, this has been a lot of work
in the making to make this workshop happen.
We have a fantastic lineup of speakers
because of these individuals who have worked very hard
in putting this workshop together.
You will find their names and their bios
in the speaker bio page on our website.
Special thanks to the National Institute of Minority Health
and Health Disparities
for also sponsoring financially for the workshop,
providing the resources needed to support the logistics
and other means so that we can put the workshop together.
I want to remind you all to please make sure
you mute yourself during the whole workshop
other than the breakout sessions later in the afternoon.
And please ask question using Slido,
which we will share with you the information about Slido
on the chat very soon.
You can ask questions during the presentation
or after the presentation.
Most of the sessions is following,
we'll have a Q&A session.
So you can post questions in Slido
and the moderator will pick up those questions,
and ask to the speakers. This is the agenda of the day,
we're going to have introduction and welcome,
and then we'll have a short overview
of the workshop following two lectures.
And then we have a lunch break,
and after that we have two case study sessions
and a breakout session afterwards.
And then we'll have a closing remarks at 4:30.
So it's a very packed day and lineup
with very fantastic speakers.
I hope you'll enjoy the workshop.
Now, let me turn to two very significant
and fantastic speakers to open the workshop for us.
The first speaker is Dr. Shannon Zenk,
the Director of the National Institute of Nursing Research.
She joined NINR in September of 2020,
following a 14-year career as a faculty member
at the University of Illinois,
Chicago College of Nursing and Institute
for Health Research and Policy.
She's also the co-chair of the NIH
Social Determinants of Health Research Coordinating Committee
together with NIMHD Director Dr. Pérez-Stable.
Following Dr. Zenk, we have Dr. Monica Webb Hooper,
who is the deputy director of National Institute
on Minority Health and Health Disparities.
She works closely with Dr. Pérez-Stable,
the director and leadership,
to oversee all aspects of the institute
and to advance the mission of promoting
the health of populations with health disparity
and health equity. Without further ado,
I will hand it over to Dr. Shannon Zenk.
Dr. Shannon Zenk: Hi, good morning, everyone.
Thank you, Dr. Choi, and it's a pleasure to be with you.
Thank you for inviting me to join today's workshop.
So the theme today focused on social
and structural determinants of health
is a distinct interest to me,
the National Institute of Nursing Research
and across the NIH.
At NINR, we recognize the importance of structural
and social factors to individual and community health,
and acknowledge
that they are major drivers of health and equity.
So I am honored to open the meeting
along with my NIMHD colleague, Dr. Monica Webb Hooper.
So while there are multiple conceptualizations
of our literature, let me share one perspective
on social and structural determinants of health.
The World Health Organization
defines social determinants of health
as the conditions in which people are born,
grow, live, work and age.
These conditions are shaped by structural factors
that comprise economic, legal, political and cultural forces
and systems such as public and institutional policies
that shape the distribution of power
and resources across society
and people's social position within it.
Social Determinants include community living conditions,
and individual family and social and economic circumstances
such as educational attainment,
social isolation, and nutrition security
are sometimes also considered social determinants of health.
Social and structural factors shape health
for better or for worse.
So that brings me to a related term
that is increasingly seen in the literature,
particularly related to health care, and that's social needs.
Some use the term social needs to refer to
an adverse individual or family social determinant of health,
while others think of social needs as downstream
manifestations of the impact of social determinants of health.
Now, regardless of whether they're under the umbrella
of social determinants or a separate concept,
it's important to note that solutions to health inequities
must include addressing upstream structural and social factors.
And we need research to identify solutions
upstream to downstream. And NIH is doing just that.
So let me share just a couple of examples.
So at NIH, we recognize the need to enhance coordination
and collaboration to accelerate research
on social determinants of health.
In support of this collaboration,
an NIH-wide Social Determinants of Health Research
Coordinating Committee was recently formed.
The RCC, which is a co-host of today's meeting,
has been embraced across much of NIH,
with 18 NIH institutes, centers and offices
serving on the executive committee.
Another NIH-wide initiative is the common funds
ComPASS program. The program aims to develop
or assess community-led structural interventions
that leverage multi-sectoral partnerships
to improve health outcomes of communities,
to reduce health disparities, and to change systems,
policies and practices to achieve optimal health for all.
So I encourage you to check out the website shown here
to learn more about the funding opportunities
through this program.
In fact, social determinants of health
are being integrated into a wide variety
of NIH-wide initiatives and programs.
These include new initiatives such as the climate change
and health initiative,
and IMPROVE, which is focused on maternal health.
So now, let me tell you a little bit about NINR
and our commitment to eliminating health disparities
and advancing health equity through rigorous research,
a commitment that we share with everyone here today.
Now, what sets NINR apart from other NIH institutes
is that our research,
with nursing's perspective at its core,
is focused on health solutions for people
in the context of their lives and living conditions
and across the many settings where nurses practice.
We are committed to leaving nursing science
to solve pressing health challenges
and inform practice and policy, optimizing health
and advancing health equity into the future.
Our mission statement reflects our belief that nursing research
is the key to unlocking the power and potential of nursing.
And our strategic plan describes how we want to leverage
the strengths and unique knowledge and perspectives
inherent to the discipline to the benefit of all people.
Within our strategic plan,
we include these guiding principles
that all NINR supported work should incorporate.
And you can see that among these is a commitment
to research that advances equity,
diversity, inclusion, and accessibility.
Moreover, one of our five research lenses
that is perspectives through
which we examine health challenges
is that of health equity. From NINR's perspective,
health inequities are rooted in structural factors,
including structural racism.
By supporting research through this lens,
we aim to reduce and ultimately eliminate
the systemic and structural inequities
that place disadvantaged groups at an unfair,
unjust, and avoidable disadvantage
in attaining their full health potential.
So we're interested in supporting nursing research
that examines structural interventions
to remove obstacles to help an increase the availability
and accessibility of resources.
And another of our research lenses
focuses on social determinants of health.
Through this lens, we aim to identify effective interventions
to improve health and quality of life
by addressing the conditions of daily life.
We want our investigators to address upstream and midstream
causes of health outcomes, identify how to limit exposure
to adverse social and economic conditions,
and how to limit susceptibility
to the biological embedding of social determinants.
All of our research lenses, which you can see here,
are complementary and synergistic.
However, we encourage researchers
to view the health equity
and social determinants of health lenses
as primary foci through which to consider the other lenses.
Our lenses reflect NINR's investment
in long-standing interest in health equity
and social determinants of health.
For example, a third of NINR's budget
already focuses on research to eliminate health disparities,
second only to NIMHD, and 26% of our budget
already focuses on social determinants of health.
So this year, we're hosting a series of webinars
led by science practice and policy experts
focus on each of NINR's Strategic Plan research lenses.
Our first lecture held in July
highlighted the social determinants of health lens,
and you can view that lecture still on YouTube
to learn more about nursing research's perspective
on social determinants of health.
And in October, we're holding our next director's lecture.
So I hope you'll consider joining us
for this virtual event,
where we'll discuss research priorities
and the practice and policy implications of nursing research
through the health equity lens.
So thank you again for allowing me to join you
and share NINR's perspective
on Social Determinants of Health and Health Equity.
It is truly a pleasure to welcome you to today's meeting.
And I'm looking forward to the rest of the day.
I'll turn it over to Dr. Monica Webb Hooper.
Dr. Monica Webb Hooper: Well, thank you very, very much.
I appreciate the opportunity to be here.
And it's great to see such a robust audience.
And of course, I'm delighted to help open the meeting
with the fabulous Dr. Zenk.
So again, thank you for the invitation.
So I want to offer a few additional points.
I mean, Dr. Zenk has already described
what we mean by social determinants of health.
And I will add that social determinants of health
are not negative, per se,
and they are distinct from health disparities,
although sometimes this is how they sort of
are discussed in the literature.
So health disparities, as we know,
refer to preventable differences in the burden of disease,
injury, violence and opportunities
to achieve optimal health experienced
by socially disadvantaged groups who often face discrimination.
And importantly, both the social determinants of health
and health disparities, by definition, are modifiable.
This means they do not have to exist they can be modified,
and we have thus opportunities for change.
So stated another way, social determinants of health
can operate to drive effective health promotion
and overall well-being in advantaged groups
such as clean air, water, great school systems, beautiful parks,
you know, appropriate social support
while driving health problems an undue burden,
or what we know as health disparities
among disadvantaged groups.
So I called the factors that drive health disparities
adverse social determinants of health.
And the goal is for everyone to have positive and protective
social determinants in their lives consistently.
Healthy People 2030 describes five broad domains
of social determinants of health and each of these broad domains
are affected by structural racism,
which also had a major hand
in shaping the distribution of money, power and resources
for the benefit of white persons in the United States
and to the disadvantage of African-American
or foundational black Americans and other groups.
The conceptualization that we at NIMHD
have about social determinants of health
is that they include both structural
as well as individual factors.
This table is a non-exhaustive list,
but it offers examples of structural factors,
such as environmental exposures,
access to not only healthcare but high quality
and culturally appropriate healthcare,
not only access to food,
but access to high quality nutritious foods,
green spaces in neighborhoods and technology access.
And among the individual factors are cultural beliefs,
social support, exposure to violence, digital literacy,
and trust or distrust, which I think in many cases
are underappreciated social determinants of health.
I want to focus in a little bit more on structural racism
and specifically racial segregation,
which is a key social factor determinant of health.
There is significant evidence that segregation worsens health.
Segregation through redlining,
created communities of concentrated poverty
with high levels of neighborhood disadvantage,
low quality housing and economic divestment.
Segregated neighborhoods with primarily racial
and ethnic minoritized residents
are more likely to have less resource
healthcare settings and school systems.
Environmental injustice in these locations
leave residents subject to elevated exposures
to physical and chemical hazards that adversely impact health.
So in turn, the physical conditions and neighborhood
environments that characterize many segregated geographic areas
make it more difficult to practice the healthy behaviors
we'd all like to see.
You may be familiar with the NIMHD research framework,
which reflects an evolving conceptualization of factors
that affect racial and ethnic minority health
and health disparities. The model is grounded
in the well-known socio-ecological model
and attends to social determinants
or what I often called contributors of health.
Much of the research has focused
on individual level biological mechanisms,
so the purple box or an individual health behavior
in the green box as explanations for poor health
that we often observe among minoritized groups
and for disparities.
As you can see, a singular focus on biology or genetics,
or individual behavior, misses the great complexity
of how we understand and address health disparities,
and does not span the other domains of influence
such as the physical or built environment,
socio-cultural environment or healthcare system,
or the other levels of influence,
the interpersonal, communities, societal, within these domains.
So consideration of social contributors
will lead us to think more holistically
and to study the same way.
Another point to note about the NIMHD research framework,
the model centers racism and discrimination
as notable social factors that affect
Minority Health and Health Disparities.
Indeed, if you look at the model,
discrimination is the only factor apparent
across all levels of influence
within the socio-cultural environment.
So how can you get started incorporating
social determinants of health into your own research?
I borrowed these slides from Dr. Deb Duran.
So thank you, Deb.
I wanted to point you to a collection of resources
pulled together and vetted by NIMHD.
We released a special collection in the PhenX toolkit,
and PhenX stands for Phenotypes and Exposures,
focus on Social Determinants of Health
with 19 instruments in a core collection
and two specialty collection
that we hope encourages standardized data collection,
quality and consistency of the data collected,
and the ability to share
and combine data from multiple studies,
as well as translational research
and effective interventions to reduce health disparities.
And we currently have a working group of experts
reviewing measures to expand the collection
with specialty collections and new measures to the toolkit.
This table provides more detail
on the measures included in the current Core Collection,
which includes 16 measures, basic demographics,
which are not social determinants,
but important that we collect and standardized ways,
access to health services, food insecurity,
health literacy, occupational prestige.
These are the two specialty collections currently available.
The individual specialty collection
includes measures for use and research
where the information is self-reported
or answered by family or caregivers.
The structural specialty collection
includes measurement and data sources,
often from publicly available datasets
at the structural or community level.
I'll close with just a few other observations about this area,
there is a need for increased research
to enhance our understanding
specifically about how social contributors interact
to affect health and health disparities.
We can make progress if we explicitly incorporate
social determinants of health,
which can inform our research questions,
our procedures, and how we interpret findings.
Longitudinal studies are very useful to help identify leaks
with adverse social determinants of health among youth,
and how they impact adult health,
of course use established measures where possible.
And as we talk more and more about health equity,
keep in mind that addressing relevant
social determinants of health is a part of equity work,
or client principles of justice, fairness,
and opportunities for optimal health in research and practice.
We know that collaborations are hugely important in this space,
and including context experts,
in addition to those with content or science,
methodology expertise.
And finally, a true focus on adverse social contributors
to help moves from a biomedical approach
to a biopsychosocial one or more of a whole person approach
and helps us make meaningful improvements
in health disparities as the goal.
So I want to thank you again very much for the opportunity
to help you start the day I hope that you enjoy the meeting,
and of course, I invite you to connect with NIMHD.
Thank you.
Dr. Monica Webb Hooper: Thank you for
your important insights, Dr. Zenk and Hooper.
Good morning, everyone. I am Constanza Camargo.
I am an intermodal investigator
at the National Cancer Institute.
We are fortunate to have a fine group of speakers today.
The full bios are available on the website.
The workshop is being recorded
and will be available for viewing in about three days.
All participants will be notified by email.
You can ask questions using the App, Slido,
you can scan here the QR code
or you can find the link in the chat.
You can ask questions during or after the presentations.
We are going to have two breakout sessions
in the afternoon.
We encourage your active participation.
And we are hoping that in sharing our ideas today,
we can make some progress in this important field.
Now, we are going to move to the first session led by Dr. Choi.
Thank you.
Dr. Kelvin Choi: Hello again, everyone.
So we are a little ahead of schedule,
which is good because we have a packed day.
So we're going to go ahead and start our first sessions,
which is exactly led by a speaker
who doesn't need no introduction.
And if I'm going to read her bio,
it's going to be like 30 minutes
of just reading her accomplishments.
So I'm not going to do that, as Costanza said,
the full bio of the speaker is on the website,
so you can actually look at all the contributions
and accomplishment that our next speaker have in her career.
Anyway, I'm still going to introduce her as a courtesy.
So we have Dr. Paula Braveman as our keynote speaker today.
Dr. Paula Braveman is a professor
of Family and Community Medicine,
and the founding director of the Center for Health Equity
at the University of California, San Francisco.
Dr. Braveman has studied and published
extensively on health equity, health disparity, racism,
and other social determinants of health.
Throughout her career, she has collaborated
with local, state, national and international health agencies
to see and conduct rigorous research
translated into practice-
Dr. Paula Braveman: Thank you, and good morning to everyone.
It's a real pleasure to be participating in this workshop.
I'm going to talk about why we should measure social
and structural factors in health research.
And I feel that my work has been made much easier
by the great presentations by Dr. Hooper and Dr. Zenk.
Next slide, please.
So there have been tremendous scientific advances
in the past 20 to 30 years that have shed light
on how social factors influence health.
And of course, there's so much that we don't know,
and that's usually what we're focused on,
but we do know a lot.
And I would say that the question is no longer
whether social factors influence health,
but the pathways and the mechanisms through
which they do that in relation to specific health outcomes,
and how best to intervene.
So this slide just lists some areas
in which I think that science has advanced
tremendously, generally over the past 20 or 30 years,
the big leaps have been taken,
that put us in a different situation
now than people interested
in the social determinants of health were in 20 years ago.
Next slide, please.
One of the areas where we have a lot of knowledge
is about how economic resources can affect health.
Now, by economic resources, I don't mean just income,
but also wealth, which is short for accumulated wealth,
the assets that people accumulate across lifetimes
and across generations,
and wealth is what you have to fall back on
when you have a time of temporarily low-income.
We have a lot of knowledge
about how income and wealth influence health,
but it is often measured very,
very inadequately in health research.
This slide just tries to give you a little whirlwind tour
through what we know about income or wealth.
So some of the things that we know are rather obvious,
although it's been important to have a literature
created that demonstrates their role.
But it's rather obvious that the more income and wealth you have,
the more access to medical care that you have, a nutritious diet
costs a lot more than a diet that isn't nutritious.
You have many more options for physical activity
when you have adequate income and wealth.
You have more options for the kind of housing
and neighborhood conditions that you can live in
and provide to your children when you have income and wealth,
and you are able to obtain lots of services that someone can't,
who does not have adequate income or wealth.
So those are what I would call kind of obvious ways
in which income or wealth can affect health.
But I think we think less often about some other ways.
So for example, how all of those factors that I just mentioned,
including the services, how they can affect stress.
If you're having trouble with your childcare situation,
or with your transportation, if you have an adequate income,
and you have wealth to fall back on,
it's much easier for you to make alternative arrangements
for childcare, for transportation.
So if you don't have adequate economic resources,
then it's stressful. That's another level
on which the economic resources can operate on health.
And we know that stress can increase instability in a family
and then family instability
becomes in turn another stressor in itself,
and we have some vicious cycles that get created.
I think another way in which literature
tells us that income and wealth matter for health
is not at all obvious, and that's in how the income
and the wealth of one's parents shape one's education.
And then education, in turn is generally
the most important factor in determining
the kind of occupation that we have access to,
and occupation, in turn is the strongest determinant
of income and wealth.
Next slide, please.
We know that features of neighborhoods
can influence health,
and we know that income and wealth and racism
powerfully determine the neighborhood conditions
that inhibit income, wealth, and education and stress,
we know that income and wealth and education
and stress and racism
are powerfully shaped by policies,
by laws and by entrenched practices.
Throughout my talk, to make it more concrete,
I will often use examples related to how racism
in particular influences health,
but the relevance extends beyond that more broadly
to how a range of social factors influence many health outcomes.
So for example, let's consider racial residential segregation.
Racial segregation was deliberately created
by explicit Jim Crow laws after slavery became illegal.
Discrimination in housing is no longer legal,
but the legacy of Jim Crow
endures in pervasive racial segregation
that is perpetuated by many discriminatory
but generally unwritten policies and practices.
Segregation tracks many black people into neighborhoods
that are less healthy in many ways,
including exposure to pollution, and unhealthy foods,
and concentrated poverty. This affects not only poor
but also many middle-class African Americans.
Segregation cuts black people off from services,
from good schools, from decent jobs,
and from economic opportunity.
All of these then perpetuate low socio-economic status,
SES abbreviated.
So all of these perpetuate low SES and poor health,
not only across a lifetime, but across generations.
And this can happen even without any individual consciously
intending to discriminate, because it is structural racism,
which is built into systems and structures,
structures such as policies,
established practices, laws and beliefs.
Most health studies do not measure neighborhood conditions
or the factors that shape them.
Next slide, please.
And next, yes, thanks. Let's do the animation there.
I would just- yeah, thank you very much.
So we know that education can powerfully influence
health in many ways.
So one way and probably the way
that is most familiar to most people
is by how education determines knowledge
and coping skills,
which in turn influence health related behaviors.
The health effects of this type of causal pathway
will generally not be immediate,
they will often only manifest over decades or generations.
Next slide, please.
And please do the animation as well.
Thank you very much.
So we also know that education can influence health
because of its powerful effects on the kinds of occupations
that one could have, which determine one's income,
and which also determine the education
of the next generation.
And this slide is a, of course, very- like the previous one,
a very simplified attempt to call attention
to just some of the many pathways
through which educational attainment can influence health,
and this is through how education can influence
health indirectly by shaping our work,
and then how the work determines the income level
and to some extent the ability to accumulate wealth,
and work also determines a whole range of work-related resources,
of which health insurance is only one.
And then the work also determines
the working conditions
and the exposure to hazardous conditions,
and also the level of stress.
So next slide, please. And please do the animation.
Thank you very much.
And another sizable body of research
suggests that education also can influence health
through a number of pathways
that we can refer to as psychosocial.
So as with the other pathways, in many cases,
the effects are not immediate,
they only play out over decades or generations.
So if you're doing a study, it's in the usual timeframe
in which we get to do our research,
which is generally not more than a few years,
you're going to miss the health implications,
and tier these- pathways are again,
a very simplified depiction of them,
but once they have been featured in the literature,
include our social standing or subjective social status,
which can shape the social and the economic resources
to which we have access to shape our perceived status,
and in virtue of their effects on all of those factors,
to shape the level of stress to which were exposed.
And another important psychosocial factor
featured in the research literature
is how social networks operate to determine levels
of social and economic resources,
but also the norms to which we're exposed,
the social support that we have access to, and stress as well.
And then I think more familiar to more people
is health can be influenced by how educational attainment
can affect our control beliefs or beliefs
about our powerlessness or power,
our sense of control, fatalism, mastery,
and how that shapes- there's a big literature showing
how that shapes our responses to stressors and our coping.
And then, of course, a very big literature
relating response to stressors and coping with health.
Next slide, please.
So, many studies measured years or levels of education,
but educational quality is rarely measured.
Very substantial evidence has revealed
that childhood social conditions shape health
across the entire life course,
generally not manifesting until mid or late adulthood.
Health Studies rarely measure childhood conditions and this,
and I want to underscore that this is one of the areas
I think where the literature is most robust
in showing how childhood socio-economic as well
as other social conditions shape our health across our lifetimes,
including in adulthood. Next slide, please.
Neuroscience has revealed that chronic,
and by chronic I mean persistent over time,
that chronic stress,
particularly if it occurs in childhood,
can lead to chronic disease in adulthood.
Neuroscientists have identified physiologic pathways
leading from chronic stress to health damage,
including through effects on neuro endocrine pathways
and on inflammation and immune system
functioning that are known to be involved in chronic disease.
Very few studies measured chronic stress
over a person's life course,
and this is another potentially very important
unmeasured social factor in health research.
Next slide, please.
Knowledge of the physiologic effects of chronic stress
has shed light on how experiences of discrimination
can lead more directly to ill health
than the pathways that I've been talking about up to now.
So let's consider discrimination based on race.
And there is discrimination
based on many more characteristics as well,
but let's focus on race-based discrimination.
Research has shown that race-based discrimination
can damage health,
it is likely not only over insults, threats, or violence,
although these continue to occur.
Some studies have shown that racism also can affect health
through the direct psychological effects of experiencing
and probably also anticipating or fearing unfair treatment.
All of these can be stressful.
And it's not just dramatic incidents.
It's the cumulative effects of daily experiences
that may be ambiguous or somewhat subtle.
It's having to be constantly vigilant
for a slight or an insult.
There's a lot of literature looking at vigilance.
Whether those slights or insults were intended or not,
and perhaps the cumulative effects
of countless little assaults on your self-esteem,
or maybe for a woman,
the effects of learning that yet another unarmed black man
has been killed by police and constantly wondering
whether your husband or your son is next.
So how many health studies measure
people's experiences of race-based discrimination
as a chronic stressor? Very few.
This is potentially another very important unmeasured difference.
Next slide, please. There also is increasing awareness
of how structural racism damages health
by transmitting social disadvantage in multiple domains
and across generations. By structural racism,
I mean racism that is so deeply embedded in systems,
laws, policies, entrenched practices and beliefs,
that it may not be easy to see,
at least not for those who are not its victims.
Examples of structural racism include residential segregation.
They also include voter suppression and gerrymandering,
mass incarceration of black men and boys,
biased lending practices that are obstacles to buying a home
or starting or expanding a business,
biased hiring or job promotions,
biased selection for elite schools,
and environmental injustice. So structural racism
doesn't need any particular individual consciously
intending to discriminate, it functions
and it is maintained by faceless systems and structures.
Next slide, please.
Now, I want to show you this diagram.
And I need to acknowledge that this diagram comes from a book
that I've written on the social determinants of health,
it's now in press at Oxford University Press.
I know that the font is rather small
in the contents of the boxes that you see in this slide,
but I want to show it to you now to give you an overview,
and in a minute,
I'll show it to you in pieces that will be easier to read.
All such conceptual diagrams and frameworks,
this one has many limitations,
and it is certainly not the only way of framing these issues,
but I hope that it makes a few key points.
First, that health and health disparities
represent the outcomes of long and often complex causal chains.
Earlier, I talked about how income and wealth,
how neighborhood and childhood conditions,
education, and racism affect health,
but what determines those factors?
We need to ask what influences the influences?
What is upstream and hard to see
that triggers the downstream causes
that are more easily observable?
So the diagram attempts to show
that the farthest upstream factors at the source,
the beginning of the causal chain are power,
and social values.
Power and social values shape systems,
for example, political systems,
voting systems, economic systems,
educational systems, bank lending,
and structures such as laws and policies.
Power and social values influence
one's access to resources and opportunities,
which in turn influence the downstream exposures
and experiences that harm health or promote it,
which in turn directly influence the biological mechanisms
that directly produce health and health disparities.
And perhaps I should take a few seconds
to define what I mean by social values.
This will mean the prevailing values in a society
about compassion, about justice, about inclusion.
I hope that the diagram suggests at least
that these long and complex causal chains
often play out not over the space of a couple of years,
but over decades or generations, so most studies will not observe
the health consequences of these upstream factors
because they cannot follow populations
for decades or generations.
So we often study- because of this,
we often study just a tiny fragment
of a causal chain that's of interest,
but we need to think about and measure to the extent
possible and acknowledge the factors that are upstream
from whatever we are able to study.
We need to acknowledge
potentially important unmeasured upstream factors,
which can make a very important difference,
the acknowledgement of what was unmeasured
can make very important difference
in how the knowledge that we produce with our research,
how that knowledge is used.
Next slide, please.
Now let's look at the diagram piece by piece.
And as I just noted,
the farthest upstream factors are power and social values
which shape the systems and the structures,
the laws, the policies, the entrenched practices.
There is a wonderful book by Professor Daniel Dawes
called The Political Determinants of Health.
So he went a step further than talking
about the social determinants of health
to talk specifically about a subset
of the social determinants which are political,
or have to do with political power.
This book shines a spotlight on the most upstream factors
that set in motion multiple causal pathways
leading to good or ill health.
The systems and structures often determine an individual's access
to resources and opportunities- that's the box on the right-
such as educational and employment opportunities,
access to earning a decent income
and building sufficient wealth,
to have economic security and to educate your children.
So let's consider structural racism
as an example of an upstream social factor.
Structural racism has systematically constrained
African Americans' economic opportunities,
their access to resources and opportunities,
resulting in less favorable education,
employment, income, wealth and neighborhood conditions.
So for example, well documented discrimination in bank
lending has made it more difficult for African Americans
to accumulate wealth in the form of home
ownership and thriving businesses.
Less home ownership in a community
means less revenue from property taxes,
which are very important source of funding for schools.
So schools in segregated areas are often under-resourced,
compromising educational quality.
Voter suppression deprives people of the opportunity
to influence policies affecting them.
It's another structural,
another manifestation of structural racism.
Gerrymandering is a system
that often makes the votes of people of color count less,
which deprives them of equal opportunity
to shape laws and policies
that in turn distribute the resources
and the opportunities that shape health.
Structural racism produces mass incarceration of black men,
condemning them and their families to poverty
and economic insecurity throughout life,
because the stigma of incarceration follows them
to their graves, denying them good jobs.
Next slide, please.
So this slide focuses in
on how the access to resources and opportunities
lead to exposures and experiences
that can either harm health or promote health.
So in turn, the lack of access
to the resources and opportunities often determines
whether someone is exposed to downstream conditions
that harm health, or promote good health.
And here, I've listed just a number.
And as with other parts of this, this diagram,
non-exhaustive list of relevant factors
that include chronic stress, environmental hazards,
school quality, housing, the quality of food,
food security, food and exercise environment,
and what do I mean by the food environment?
I think most people have heard of the concept of a food desert.
Actually, the literature is somewhat inconsistent
on the role of food desert,
meaning the lack of full-service grocery stores,
the role that that plays in health disparities,
but where the literature is very consistent
is in the role of food swamps.
In other words, food swamp would be a community
that is full of convenience stores, liquor stores,
and fast-food places.
And there the literature is really pretty consistent
about that as a factor that affects health.
Other health-harming or health-promoting factors
certainly include access to medical care,
but also the quality of medical care,
our social networks and our health-related behaviors.
Next slide, please.
So, this then depicts how exposure
to the health-harming
or the non-health promoting conditions
in turn triggers the biological mechanisms
that directly produce good or poor health.
These mechanisms include neuro endocrine,
immune and inflammatory processes,
and other processes.
So next slide, please.
Now, I want to take a quick look again at the whole diagram.
So most biomedical research focuses exclusively
on the biological mechanisms and their health effects.
Some public health studies look at the role of access
to resources and opportunities that shape health.
A lot of public health research
focuses on how downstream health-harming
or health-promoting factors damage health,
but it is very rare for a health study
to go all the way upstream and examine
or at least acknowledge the roles of power
and social values,
and what Professor Dawes has called
the political determinants of health,
which drive the systems, the power policies,
the laws and the pervasive and entrenched practices.
Next slide, please.
So what happens when we don't consider
the influences on the influences?
How often do you read scientific papers
concluding that an observed racial difference in health
must reflect underlying biological differences
or behaviors,
because the researchers saw a racial difference,
even after they controlled for SES?
It is not possible to control for SES,
SES is too multifaceted,
it is not just your income or your education,
but both and your accumulated wealth.
It's also the quality of your education,
your neighborhood's socio-economic conditions,
and your parents' education, income and wealth
when you were a child.
It is all of these factors and more throughout a person's life,
and none of us can capture all of it.
Because of racism and largely because of structural racism
at the same level of education, black and Latino
people have far less income than white people do,
and at the same income level, black and Latino
people live in unhealthier neighborhoods,
and have a fraction of the accumulated wealth.
All of these differences can affect health,
yet these factors are rarely measured,
but studies often conclude that racial difference
is genetic or behavioral
if it persists after control for SES.
So race, the race variable,
which is often seen in health studies,
that variable is often capturing unmeasured socio-economic
and other social factors,
including important structural factors.
Next slide, please.
So, when we fail to consider the upstream factors,
our results can mislead policymakers,
which can result in wasted resources
and in needless suffering.
So when we don't consider the upstream factors,
we contribute to the continuation of policies
that persist in focusing on downstream solutions
and mechanisms
while upstream forces and factors are untouched.
So the upstream factors continue to generate
the downstream conditions
that trigger the mechanisms that harm health,
or that fail to promote it. I also want to note
that when there are important unmeasured social differences,
including structural differences,
our results also may unintentionally feed biases,
including unfounded biases about the basis
in racial disparities in health.
Next slide, please. So what can we do about it?
The fact is that none of us
can study an entire causal pathway for many reasons.
One is how long they are, you know, playing out over time,
but also their complexity.
And the- and I would like to note here
that in a lot of the diagrams that I've shown you,
these are gross simplifications of the causal pathways
that are involved in the effect of social
and structural factors on health,
none of my diagrams showed the interactions
between different factors that were listed on the diagram,
or the interactions with factors
that weren't listed on the diagram.
So it's very complex.
But although no one can study an entire causal pathway,
we can at least consider what's in it,
and we can frame both our research questions
and our conclusions accordingly, connecting the dots
and acknowledging the limitations that are there.
So for example, we can acknowledge
that the race variable
is very likely capturing unmeasured childhood experiences
and direct and indirect experiences of racism.
So I want to appeal to you that
when you read or you conduct studies
including variables representing race,
you need to be aware that regardless
of what else it may represent,
the race variable is always picking up
the totality of unmeasured experiences
and exposures that a person of that race
may have had throughout their life pores,
which could have had health effects.
And even the smartest researcher could have measured at best
only a tiny fraction of those experiences and exposures.
So I want to urge you to ask always, are you looking at race?
Or are you looking at racism, including structural racism?
I think that another thing that we can do is to collaborate,
those of us in the health field or training in the health field,
we can collaborate with social scientists
who can help us to be aware,
more aware of unmeasured differences
and the implications of those unmeasured differences
for the conclusions that are reasonable to make from a study.
Next slide, please. So as I noted earlier,
the question is no longer do social factors,
including structural factors affect health,
but how do they operate in relation
to different specific health outcomes?
And how best can we intervene equitably,
effectively, and efficiently?
So I do think that the paradigm in health research has shifted,
giving more attention to social and structural factors.
I'm showing you this cartoon, and I like this cartoon,
because it captures the notion
that although we have made progress in understanding
how social and structural factors shape health,
we are still, unfortunately, a very long way from seeing
that understanding translated into better health,
and particularly into greater health equity.
So thank you for your attention.
And I'm very much looking forward to the Q&A.
Dr. Kelvin Choi: Thank you very much, Dr. Braveman,
a very- of course very insightful,
and a broad lecture and kind of gave us an overall sense
of how we address the issue and how we actually
examine the effect of social determinants
and structural influences in our research,
you know, from basic science to clinical science
to publishing science and even in interventions.
I mean, one of the key message that you point out there
is really be very precise about what we measure,
don't use some of the-
some of the measures we have used in the past
may pick up a lot of things
that we don't even know what they are.
And being precise is very important,
so that we know, are we actually measuring race,
or are we actually measuring racism?
Which one are we actually studying?
One of the questions came from the audience,
which is an interesting one, and I'll pose this to you.
The question worded as, do we still need more research
showing how racism affects chronic health?
Or do we need new policy or structural changes?
And I think the question to kind of rephrase
it a little bit is, well, do we know enough
or do we still need to know more in terms of research?
Or, and at the same time, how do we conduct research
related to policy and structural changes
that impact health disparity?
Dr. Paula Braveman: That is a very interesting
and important question.
And my answer would be that we need both.
One of the reasons that we need to continue
to demonstrate the toll that racism takes on health
is because the public has very short attention span
and needs to be reminded by fresh results
constantly about what the associations are,
so it's not enough to just cite past research.
But of course, we also need the more upstream research
and to show- to shed some light on what will be necessary
in terms of policy changes to do something about it.
I think that's always there, that question is often there
for people who do this kind of research:
Why do I need to be demonstrating this again?
And isn't the moral arguments against racism enough?
But unfortunately, in this society,
it isn't enough,
and demonstrating and continuing to demonstrate the toll
in terms of health that it takes is important,
and speaking to what the implications
are in terms of the waste of human resources.
Dr. Kelvin Choi: Yeah, that is a continuous struggle in terms
of continuing to provide the evidence to showcase
to how, in fact, racism for what mechanism impact health
and without evidence,
we may be able to move the policy agenda a little further.
And other questions came in asking about
what additional measure would you suggest to use
to measure racism when also measuring race?
So let me repeat the question,
what additional measure would you suggest
to be used to measure racism when also measuring race?
Dr. Paula Braveman: Well, that's another
very important question. So there are some validated
measures of experiences of racism.
So it depends upon what you mean by measuring racism, there's-
we can measure the extent to which a society is racist,
and that's different from measuring the extent
to which an individual has experienced racism
in different forms.
I think that legal people and policy experts,
you know, collaborating with them can be very helpful
for us in sort of figuring out what the, you know,
the extent to which a given system
or structure is racist.
And we always need to think of the difference
between de facto racism and de jure racism,
so racism that's there in the laws,
you know, that you can point to in the laws versus racism
that it doesn't appear in any law,
it's just deeply embedded in historical structures
and has tremendous power in that way.
So we need to measure both of those,
the de facto and de jure racism in a society.
In terms of measuring the extent
to which an individual has experienced racism,
there are some well validated instruments,
and I think David William's measure of everyday racism
is one of the most utilized and it's one of the best.
I think Nancy Krieger has also done some great work
to measure racism.
The limitation with those, though, I must say
is that they're very long, their instruments are very long.
And most of us actually cannot use an instrument
that long to measure racism, which is one of many variables
that we need to measure in our society.
So there are also a number of more brief measures
that get used,
and you can find those in the literature.
I mean, what about- you know, the rest of your question was,
well, as I understood it, how do we measure racism,
but how do we measure race?
And I think the most important thing about measuring race
is realizing that it is a social construct,
not a biological construct, and that in measuring race,
we are measuring the kinds of experiences and exposures
that someone of a given race is likely to have had,
and too often there's an assumption
in biomedical research
that racism is like age, race- age, race, sex,
or maybe age, race, gender, you know, that those are just-
you always need to measure those,
and it's kind of a knee jerk response
without thinking about why you need to measure those
and without thinking about how grouping race with age
and gender or sex reinforces the unfounded notion
that race is biological.
I think that a lot of us think that when we're measuring race,
what we're doing is incredibly crude,
and that is, we are measuring the large geographic area,
generally a continent, but not only a continent,
that a person's ancestors are from,
and not that we're measuring something biological.
There are secondary- superficial,
secondary physical characteristics
that map out to some extent
according to your geographic area of ancestry,
but no one has demonstrated
that those secondary physical characteristics are associated
with important underlying biological differences.
Dr. Kelvin Choi: So Dr. Braveman,
you have- in your response,
you point to an interesting, and a related question,
which is the idea of intersectionality.
So how do we study?
So when we talk about measuring race,
but there's also other factors that intersect with race
when we think about race and how racial identity intersect
with sexual identity and intersect with class,
with income and education.
Can you talk more about how we can better understand,
you know, these different aspects
and different identity influence health?
And how do we actually measure and study them?
Dr. Paula Braveman: With that kind of question,
you're going to really make people afraid to tread
on this territory, because it's so complicated.
But the fact is, we do know enough to say
that it's not just your race, and it's not just your gender,
and it's not just your sexual orientation,
or your gender identity,
but all of the above and that they interact with each other.
I think this term intersectionality has-
well, it's a code word for referring to the interactions,
and the interactions among the social advantage
or the social disadvantage that one may have in virtue
of the different aspects of one's identity.
And when we don't take that into consideration,
we can be way off the mark.
I think, here too, as with looking at racial disparities,
it's not just measuring these different aspects
of a person's experience and identity,
but it's also because we won't be able to measure all of it,
it's acknowledging that we haven't measured
this which could be very, very important.
So I think it would improve our research so much
if we were more conscientious in thinking
about the multiple and interacting upstream factors
that can affect a person's health,
but also, so not just trying to measure
as much as we can of that, which we should,
but also acknowledging what we can't measure.
And I want to underscore, I think it was Dr. Hooper
who mentioned the PhenX social determinants measures,
this is a very important resource for investigators
who want to take these issues seriously,
and I think NIMHD deserves a real pat on its back
for having sponsored that.
But if you're looking for ways
to measure different social variables,
you've got the results of a lot of thinking
by subject area experts,
and it can save you a lot of time
and a lot of wasted effort.
So if you Googled PhenX, P-H-E-X
and then X, PhenX SDOH for social determinants of health
or you just Google PhenX social determinants,
you'll come to the toolkit,
which is the collection of measures.
Dr. Kelvin Choi: Thank you, Dr. Braveman,
for your wonderful talk
and also for your endorsement of the PhenX toolkit.
It is definitely a lot of work by the colleagues at NIMHD.
We are unfortunately running out of time,
I know that we continue to have Q&A,
this can last a whole workshop,
but we do have other interesting sessions lineup
later this afternoon. So thanks again, Dr. Braveman,
and we will make the information about the PhenX
toolkit available on the workshop website,
so people can go there and look for it as well.
Once again, thank you, Dr. Braveman.
Thanks for the wonderful talk and your insight.
Dr. Paula Braveman: Thank you.
Dr. Kelvin Choi: Now we turn over to Dr. Norton,
who will be chairing the next session.
Dr. Jenna Norton: Thanks, Dr. Choi
and Dr. Braveman
for that excellent opening to our workshop and overview.
Great talk as always. I am pleased to be here.
As Kelvin said, my name is Jenna Norton.
I am with the National Institute of Diabetes,
Digestive and Kidney Diseases.
And I'm pleased to be here to chair a panel,
which I think we had a great segue
with this discussion into on the measures and methods
on integrating social determinants of health
in biomedical research.
So I think that discussion
queued this panel up very nicely.
So I'm pleased to introduce our three speakers.
First, we have Evelyn Gallego,
who is the CEO and founder of EMI Advisors,
and the program manager for the HL7 Gravity Project.
She'll be sharing her work and the work of many others
on developing data standards for integrating
social determinants of health into electronic health records,
which may be another potential future source of data
for this area.
We will also hear from Dr. Patricia Keenan,
who is a senior researcher at the Agency
for Healthcare Research and Quality.
And she's here to tell us about AHRQ's small area
database for measures on social determinants of health.
And finally, we'll hear from Dr. Scarlett Lin Gomez,
who is a Professor and Vice Chair for faculty development
in the Department of Epidemiology and Biostatistics,
and a member of the Helen Diller Family
Comprehensive Cancer Center at the University of California,
San Francisco,
and she'll be telling us about some of the key methods
she's been using to integrate social determinants of health
into her research. So Evelyn, please take it away.
Evelyn Gallego: Thank you-
Dr. Jenna Norton: Evelyn, you're on mute.
Evelyn Gallego: Yes. Thank you so much.
And good morning, everyone.
Thank you so much for the opportunity to speak today
about the Gravity Project,
very timely and aligned with the presentations we've had to date.
So just quickly, I know we have limited time, a quick agenda,
I will go over the Gravity Project
for those not familiar with what it is,
talk through our accomplishments to date,
our success factors for scalability
and how our work aligns to the Department of Health
and Human Services or HHS SDOH strategic approach.
So it was very nice to hear from our presenters earlier
that did a terrific job going through all these terms.
And we like to, when we kick off the Gravity Project
is ground on these definitions and terms,
because we do recognize as a project, as you'll learn,
that we are working on creating a common language
so that the terms can be captured electronically.
And we've noted in the past two years,
that more of these terms are being used interchangeably.
Again, perhaps this is not happening on the research side,
but we are seeing it as we work on data standards
that we have these terms now being integrated,
or called in we have a growing-
So we do want to recognize that there is a-
you know, and as presented earlier,
there are specific definitions for these terms,
and we'd like to define them at a very high level.
So I won't go through all of them,
but acknowledge that when we refer to health equity,
it's really at a systems level, right?
So we talk about health equity, meaning when every person
has the opportunity to attain his or her full health.
But when we talk about health equity at a community level,
then we speak of social determinants of health,
which we all know are defined by the WHO
and the CDC as the conditions in which people are born,
grow, live, work and age.
But then SDOH is further defined at an individual level
as protective factors, social risks and social needs,
again, lots of discussion on what these terms mean.
What I do want to acknowledge here,
and again, we've heard this is that health equity is advanced
not only by addressing the social determinants of health,
but also how we improve access and quality of care.
And again, this ties very much what we just heard
from Dr. Braveman, Dr. Zenk, and Dr. Hooper.
So I won't spend too much time here,
I think all of us joining today are really clear on
why addressing or why SDOH are important.
You may already be familiar with the visual on the right.
I'm here more to acknowledge that again,
the interest in health systems, right, and addressing
SDOH has even, you know, occurred before COVID.
And if you go through the history
of what the Gravity Project is really very much grounded
on the interest of health systems
to address the individual social needs
as they started to shift
from fee-for-service models to value-based payment.
And of course, we have a lot of growing evidence
around how unmet social needs
negatively impact health outcomes.
So again, won't spend too much time here.
I think we spent a lot of time.
What I do want to spend time on
and the purpose of this discussion
is that even though we say there is this business case,
it is very clear why capturing this information
in electronic systems is important,
challenges remain on the ability for existing electronic systems
to capture this type of information
and exchange it across with other systems.
And so this slide is really to acknowledge
the two challenges that the Gravity Project
was set up to address
and it's around the standardization
of the data collection and storage
and the sharing of this data across ecosystem parties.
We're not grounded on addressing
all these other challenges as listed here.
It's really to say that there is-
it's important for industry
as a whole to be able to continue to work
on addressing these challenges to get us to ultimately
to a place where we can use this data
not only for patient care, but also for research.
So it brings us to what the Gravity Project is.
Here's our mission statement,
it is to advance and promote equitable health
and social care by leading the development and validation
of consensus-driven interoperability standards
on social determinants of health.
Our project was initiated in 2019, May 2019.
It came from- and not listed on this slide,
it was work from evidence gathered from Dr. Laura Gottlieb
at SIREN, Social Interventions Research
and Evaluation Network at University of California
in San Francisco, and Dr. Caroline Fichtenberg.
They had identified through their work
the lack of codes available or data standards
to represent this data. And with that reason,
and as we go through with initial funding
from Robert Wood Johnson Foundation,
they came to my team and I at EMI Advisors
to help them solve this problem. Right?
So we say our scope of our project
came to be around data standards and data standards to represent
and exchange patient level or individual level data
documented across the four clinical activities of screening
for social risk, assessment, diagnoses, goal setting,
and treatment or interventions.
Our scope is also to test and validate
this type of data for use not only in patient care,
but to support care coordination between health
and human services sectors, population health management,
public health, value-based payment,
and of course, clinical research.
To date, we have completed data concepts
for 17 social determinants of health domains,
they're those small little icons you see on the right-hand side.
These domains initially are grounded by those identified
in the 2014 National Academy of Science and Engineering
and Medicine, or NASEM,
capturing social and behavioral domains
in electronic health records
that helped inform the standards referenced in the Office
of the National Coordinator certification program.
To date, we've also been working on incorporating the domains
as defined by Healthy People 2030.
And here's our conceptual framework.
We talk about this in all our discussion.
So our framework is centered around the concepts
that can be documented
and shared across those four activities.
I mentioned screening, diagnoses, goal setting,
and interventions.
And this is regardless of the input system.
So it could be an individual or patient's digital app,
clinical providers electronic health record,
a social service provider's IT system
or community referral platform.
So we say we are agnostic to the systems and tools
being used in the field,
and the importance is capturing this data in a standardized way.
And as I mentioned, we do this to support
this limits exchange of data following.
And we talk about our work in terms
of supporting three overarching use cases.
The first one listed here is gathering the SDOH data
in conjunction with the patient encounter.
The second use case is documenting
and tracking the SDOH
or social needs-related interventions to completion.
This would include, for example, a referral from a health system
to a community-based organization.
And last use case is the ability to gather
and aggregate the SDOH data for uses beyond point of care
to include clinical research,
public health, population health.
So we're grounded around these activities,
and we just completed for example,
although this is very clinically grounded,
we just completed public health.
So an extension of the third use case,
we worked with the CDC on creating respective
SDOH public health use cases to support public health.
And we'll talk about how we can do that for research.
So we execute our work
within three work streams as listed here.
We talk about- the first work stream
is the terminology work stream where we focus on defining,
creating these data definitions,
identifying codes, submitting for the new codes,
working with the coding stewards,
and the coding systems are the medical terminology
SNOMED CT, ICD-10 and LOINC,
and then publishing in the National Library
of Medicine Value Set Authority Center.
The other work stream is our technical work stream
where we primarily as an HL7 FHIR accelerators,
so we became a FHIR accelerator in August of 2019.
So we are focused on the technical standard
creating HL7 FHIR specifications following the HL7 process.
Our third workstream are activities around testing,
as I mentioned, our scope is to validate these standards
are the right standards in the field,
and this is our pilot workstream.
So we do all this collaboration through these three workgroups.
To date, we have convened over 2,500 individuals
from across the health and human services ecosystem.
And when I talk about this, it's not just the health
IT vendors or the health systems,
this is community-based organization,
these are researchers, these are state agencies,
these are health plans, these are payers, these are vendors.
Again, many of them joining us who didn't even know
what a standard was or a coded terminology.
We've convened them for the last now three years.
We convene through the terminology workstream,
which we meet bi-weekly through public collaborative,
this is currently on hold.
So you see that in red here because we are fundraising.
So I'll talk through what that means.
We also have a technical work stream facilitated
through weekly calls, managed through HL7.
And I will announce later on that
we have a pilot's workstream
that we meet monthly starting this week.
So everyone is welcome to participate in these.
These are not siloed or closed meetings.
Everything is recorded and made available.
One of our core values is having open transparency,
public calls, that anyone can leverage our materials.
So, our project, we are a project, so we're not an entity.
We are funded through in-kind and sponsorship.
I mentioned we initially were funded
for the Robert Wood Johnson Foundation.
To date, we receive support from multiple stakeholder groups
as listed here, health plans, provider, groups, associations,
payers, technology vendors,
and of course, the federal government.
So you'll see a list of HHS agencies on the right-hand side.
Here's our roadmap for 2022.
So I mentioned those three work streams.
On a terminology workstream, this year,
we completed three new domains, health literacy,
health insurance coverage status-
oops! sorry- medical costs burden,
and we spent the summer months building.
So what we say is when we meet, convene the public,
we bring them then together to help us define the concepts,
we bring subject matter experts. And then in the background,
we then start working with our coding stewards
on what new codes need to be developed,
and submitting them for publication.
We are currently on hold as we look to launch the next domain,
which is digital, and equity, you see it pending,
because we are in the process of fundraising
to completely launch that.
And I'll go through what other domains are in our pipeline.
On the technical work stream,
we have completed publication of the HL7
SDOH clinical care FHIR implementation guide,
it was published as a standard for trial use
Version One in August of last year.
We are in the process of publishing the second version
of that FHIR implementation guide for this fall.
And then we are also very eager to start-
kickoff our pilots workgroup this week.
Okay. So on our terminology side,
I want to acknowledge that we do have a rigorous process
for developing coded concepts.
The visual you see on the right-hand side
illustrates how this applies to the clinical workflow
as I presented in our framework.
This means that as we look across,
screening concepts are coded in
LOINC, as we go through diagnoses,
they're coded in SNOMED and ICD-10,
particularly ICD-10 Z-codes, goals are coded in
SNOMED CT, and interventions are SNOMED-CT.
So, to date, as I mentioned,
what you see on the left-hand side,
we've completed data definitions for 17 domains,
we have 16 LOINC screener codes available,
14 ICD-10, Z-codes,
and eight were included in the fiscal year
2023 CMS IPPS rule, SNOMED
CT, we have 16 intervention codes available,
over 140 value sets in the National Library of Medicine,
and very exciting that our data class was included
in the Office of the National Coordinator,
US corps for data interoperability version
two and carries over-
right now they're getting ready for V3.
So ultimately, for us today is really to acknowledge
that there are coded concepts available,
they're published, they're ready for use,
and they can be incorporated in data collection and exchange.
One of the key activities our team did as they worked with
SNOMED CT on interventions
was thinking of how best to group these type of activities.
So the Gravity Project came up with nine
or eight intervention types as listed here.
This is to acknowledge that referral being one of,
I would say the most common intervention type
as referrals are being carried out from the health system
to a social service provider, community-based organization.
But there are other intervention types.
And again, these are all coded by
SNOMED CT codes, and they have specific definitions.
So when we have coded concepts and we publish them,
they are grouped in this way.
So what does our next steps look like?
So this is to acknowledge that as we fundraise to kick off
the digital and equity domain,
we've also been working with our governance committees
on what are the other domains in the pipeline.
So what you see here on the left-hand side,
and the green is, you know, upcoming,
so very timely to be talking about racism,
bias and discrimination,
that is a domain that we have in our pipeline.
I know it's of strong interest
to even one of our sponsors, RWJF.
We've also been looking at green space
and adverse childhood experiences.
Other ones that are being proposed
have been expanded material hardship,
incarceration, migrant laborers,
again, very much tied to Healthy People 2030.
Activities that are proposed moving forward
is to collaborate on shared ontologies and open taxonomies.
So not everyone is using SNOMED
CT, ICD-10, the medical terminologies,
we acknowledge other taxonomies, such as the 211
LA taxonomy that's being currently in use
right now by many community-based organizations,
and it needs to be readily
mapped to the medical terminologies.
We've also been looking at developing
supplemental terminology
deliverables to address sharing information
on social care program eligibility and enrollment.
That's currently not in scope for gravity,
but it's of interest to our community.
And lastly, we want to participate
in quality measure development to lend coding,
content and measurement insight.
Our terminology director, Dr. Sarah DeSilvey
does participate in some measurement discussions,
but we've not brought it into our community
because our scope has been the data standards
to support individual level documentation
as part of a clinical encounter.
So I'll move on to our technical work stream
acknowledging that our work is supported by NewWave Saffron
and the American Medical Association.
Our implementation guide just here is to acknowledge
we do have a current standard published through HL7,
these are open standards for implementers to use.
The IG supports all the clinical activities
I mentioned, assessments, diagnoses, goals,
but it also incorporates consent,
tying into other national standards
for capturing individual consent.
And it also has draft specifications
for capturing race and ethnicity data.
So again, not race and ethnicity,
completely agree with the comments made earlier,
they are not social determinants of health,
but there is data that needs to be captured
as part of the record
and the information that is exchanged about an individual.
We look to the research community
to help better define what those concepts need to be.
I think there are standards that exist,
but they're not commonly used across systems.
And we're in the process of targeting a publication
for fall of this year.
This is just a high level, the testing interactions.
So it's to acknowledge that the implementation guide
has guidance around how a FHIR-enabled entity
can exchange with another FHIR-enabled entity,
for example, a clinical site
sharing with a coordination platform
or another clinical site or a payer.
But it also acknowledges that many of the interactions
will be between a FHIR-enabled system,
and when that is not FHIR-enabled,
like a community-based organization
that will never adopt the FHIR standard,
because they have no need to base on their population groups,
and policy around what standards they can use,
but the FHIR IG has some guidance
on how to support those data transactions.
Here are technical considerations or activities
under consideration for the technical workstream.
So we want to do more,
we propose to identify methods of anonymization of data
to support reporting for public health.
We want to develop new use cases and requirements for aggregation
and de-identification of SDOH information
for population health and research.
We want to develop and incorporate new use cases
that support existing FHIR IG.
So our FHIR IG, again, grounded on the clinical encounter,
clinical to social care provider,
but we have a strong interest in the field
about supporting data
exchange between one community-based organization
and another community-based organization
never touching the health system,
as well as a CBO exchanging to a state agency
that is very common.
And particularly for one of the use cases,
one of our partners is working on foster care for children.
So our pilots work stream,
so we launched officially this week.
So this is an alert to all of you
if you want to follow testing in the field,
we launched this week. Our pilots affinity workgroups
are as a peer-to-peer learning forum for entities
participating in the real-world testing of the standards.
It's also established to foster a collaborative
learning environment for pilot participants.
So we've not had this opportunity in the past,
a lot of the standards are developed and published
by the standard development organization like HL7,
and you know, there's forums for learning but it's not broad,
and again, it's mainly targeted to the implementers themselves,
but this will be open.
And I do want to acknowledge, again,
the support of the Office of the National Coordinator,
the Administration for Community Living,
IHE USA and AARP, in supporting this work.
Levels of participation, again, open,
and individuals can join as an observer
and just follow along and hear from pilot sites
on how they're testing and implementing the standards.
And then we have the actual pilot participants
that will be testing in the field.
And again, all are invited and the link is here at the bottom.
I think these slides will be shared with you afterwards.
This is what our tiered piloting approach looks like.
We've developed a tiered approach really with the premise
that we want to meet entities that are testing
and implementing the standards where they're at. Right?
We know FHIR is required as a standard for data
exchange in the US,
but you know, not everyone is there,
people are starting to update their systems.
So this is really to allow an incremental way
for entities to test through,
you know, just testing the terminology.
This is what tier one is, this is really, you know,
how you define the concepts in your system,
are you all using, you know, the same language,
we say, and your system can understand and read it?
The second tier is really to support
existing data infrastructure.
So, for example, you know, we have recognized standards
like the HL7 Clinical Document Architecture, CDA,
we have HL7 V2s that are commonly used right now,
particularly for ADT feeds.
And we have the direct transport.
The third tier is the full FHIR model.
So what are success factors to date?
We say that, you know, it's great,
we've published the standards,
we have a lot of standards available,
but their value really comes into the integration
into several areas.
So we talked about integration at the policy level,
I mentioned USCDI already,
within payment models, within programs.
Again, we need these levers to promote
and encourage the use of the standards.
Also the standards to be called out in other standards,
Gravity is a FHIR accelerator.
There are other five HL7 FHIR accelerators.
And so we work closely with them.
Inclusion of the standards in grant programs.
So we do have examples.
I'll talk in the next slide, how they've been included.
Where we need to do more work is at the practice level,
so having repeatable process for adopting,
implementing and using the standards and this type of data,
and how we encourage or influence innovation
in the market around creating new tools
for capturing aggregation of the data.
So this is just quickly how they've been included
in policy programs and grants.
So I already mentioned USCDI data classes.
So they're there.
We have talked through about CMS
having it in their Inpatient Prospective system,
so they point to USCDI.
CMS also had their Medicare Advantage
and Part D final rule that requires special needs plan
to use the standardized questions,
again, pointing to USCDI version two.
And then the Gravity standards
have three active grant programs right now,
or I would say challenge programs.
The Administration for Community Living
has the social care challenge grant,
as a requirement for the grant, they had to adopt the standards.
ONC has the Leading Edge Accelerator projects,
they have requirements to adopt the standards,
and the Administration for Children and Families
has an innovations grant right now
that they also have the requirement
to adopt the Gravity standards.
We also like to talk through of how our work
to date aligns with national.
So this year, HHS published the HHS
strategic approach to address social determinants of health,
where they acknowledge that addressing
SDOH involves coordination across the sectors.
So I'd like to demonstrate this. We saw this visual earlier on
but I really talked through about it
in terms where Gravity Project aligns.
And we really say we are supporting the midstream level,
where we are developing data standards to support health
and human services integration and creating that language
needed to support interoperability.
And then here is just a snapshot of how we align to goal one,
we're directly supporting a robust
and interconnected data infrastructure
to support care coordination and evidence-based policymaking.
I think that concludes my presentation.
Thank you all and I'll wait for Q&A later on.
Dr. Jenna Norton: Yes, I think we'll have time
for Q&A at the end.
So next up, I would like to invite Dr. Patricia Keenan,
to speak, please.
Dr. Patricia Keenan: Thank you, Dr. Norton.
And thank you so much to the NIH
health disparities interest group
and the NIMHD for the opportunity
to talk about our work on the AHRQ SDOH database.
Social determinants of health is an important focus area
across the Agency for Healthcare Research and Quality or AHRQ.
And SDOH contributes to AHRQ's mission,
which is to produce evidence to make healthcare safer,
higher quality, more equitable, accessible and affordable.
Next slide, please.
So just briefly, I'll go through some context
around social determinants of health.
And I'll move quickly through a series of slides
that describe contents of the SDOH database,
and then talk at the end about some examples of data uses.
Next slide, please. Great.
So if you look at the map on the right side of the chart,
this is showing the relation
between premature mortality deaths below age 75
and social vulnerability across counties in the US,
this is using the 2018 CDC social vulnerability index.
And what you can see here
is that the areas that are shaded in dark purple
are the areas with both the highest premature mortality,
as well as the highest social vulnerability.
And I suspect that with this audience,
these long-standing differences
and the role of social determinants of health
and contributing to health outcomes,
as well as the geographic concentration of that impact
is all, you know, familiar to this group.
But what I would pose is that even with a growing momentum
around social determinants of health,
there's still substantial opportunity
remaining to expand the use of community level's
social determinants of health data,
to generate evidence on how best to actually address
these differences, and improve health.
And that's really the focus of SDOH database
is to create a standardized community level resource
pulling data from multiple public sources,
and spanning the SDOH domains of social context,
economic contexts,
and et cetera, shown on the slide.
So the purpose really is to make SDOH data easier to use,
both so that community level data can be included in studies
to account for contextual differences across areas,
to improve the robustness of study findings,
as well as to contribute to studies
that are working to identify effective interventions,
and ultimately inform efforts to improve health outcomes
and health equity.
A key aspect of the SDOH database
is that it's linkable by geography.
Currently, data are available at a county level,
zip code level, and census tract level.
So this is my sort of SDOH database in one slide,
and I'll go into more detail, but as I noted,
move through relatively quickly, and the slides will be available
after the workshop for reference, for more information.
Next slide, please.
So the basic approach we've taken thus far,
this is a relatively newer project at AHRQ,
and so we've worked to develop the database,
make it publicly available
and iteratively improve it over time.
We began with an environmental scan,
we released initial data files in 2020,
and just put out a revision this past summer.
And I think I covered the approach on the prior slide.
Next slide, please.
So just to, you know, show again,
the geographic levels, county, zip code, census tract level,
we've expanded the geography over time,
and we're looking to get still more granular in future work.
Next slide, please.
So this slide is showing more detail
underneath the social determinants domains.
We have a series of sub topics
and these are coded into the code books,
so that you can actually look for subsets
of variables on specific topics. I won't read through this,
but this gives, you know, a flavor of what's available.
Next slide, please.
Here, we're showing the number of variables
available across the different geographic levels.
I suspect it will not come as a surprise
to you all that there are-
is the greatest number of variables at the county level,
and the availability is lower at the zip code
and census tract levels. Next slide, please.
Here we're showing a sampling of data sources
within the SDOH database.
This is ranging from a number of different
executive branch departments,
as well as some external sources.
Next slide, please. To provide a little more detail,
you can read through this slide at your leisure,
I'll just note the first row, which is showing
some of the different socio-economic indices
that are available within the SDOH database,
from the Census Bureau as well as CDC.
And I'll use the social vulnerability index again
in a couple more slides later in the presentation.
Next slide, please.
So here, we're expanding on additional examples
focusing on health care context.
And they will know that in addition
to aspects of the healthcare delivery system,
we also have included information on health behaviors
and health outcomes, health outcomes, you know,
in case you're thinking yourself
that's not a social determinant, that is, of course, correct.
We did include some aggregated information
on health outcomes and health conditions
because we thought it would be helpful for people
to be able to look even just within the database,
to see some relationships between social determinants
and health status and outcomes.
Next slide, please.
For the data source documentation,
we have a couple of different files.
One is a PDF that we refer
to as the data source documentation file.
It contains information source by source
on the different variables included
and, you know, sort of caveats
and things to be aware of with each of the sources.
We also have codebooks available by year,
and those can be filtered, as I mentioned earlier,
by SDOH domains and subtopics.
Next slide, please. So I won't talk through this,
this is more of a, you know, sort of user reference for,
you know, ways to find different types of information
within the documentation and codebook files.
Next slide, please.
So as we worked to assemble the database,
we thought about characteristics
to make the data readily available.
And so there are a couple of aspects of standardization
that we've implemented.
One is that the naming conventions
are standardized across all of the datasets,
and that makes it easier to look across the different variables
and see what regional data source they came from,
as well as have interpretable name,
that is, you know, reflective of the concept
underneath the variable.
Another aspect of standardization
is the geographic level.
So in some cases, that required geographic transformation
into the geographic levels of the database.
And one other piece that I'll mention
that's not listed on the slide is the data years,
some data sources are released as of a given year,
but the underlying sources can vary by year.
And so the way we've assembled the data is all,
you know, sort of consistent
with the calendar year of the data source,
the underlying data reflect that data year.
Next slide, please.
So we'll turn to just a couple of brief examples of variables
within the database
and talk a little bit about potential for data linkages.
Next slide, please.
So this first slide is showing the- sorry,
a little block on my screen- is showing broadband access
and the poverty rate across counties in the US.
And so basically, what this is showing
is in the darker areas on the screen,
areas that have higher poverty and lower broadband use.
And you can also see from the sort of purple shaded areas,
places that are lower broadband, but are not-
are lower poverty areas.
And so, this kind of a map can show geographic,
you know, sort of access to broadband
reflected through different sources of potential,
you know, sort of characteristics,
like number of ride,
that can create barriers to broadband access,
which, as I think we've seen through the pandemic
is an important contributor
to people's well-being in terms of access to,
you know, education, healthcare, et cetera.
Next slide, please.
This next chart just briefly is showing track level annual
means of air particulate matter concentration across the US.
And we here can see just, you know,
sort of quite starkly the higher particulate matter
in the western part of the US reflecting wildfires.
And you can also see some darker shaded areas
across the US highlighting locations of cities.
Next slide, please.
So this chart is just an example to show that,
as we've heard from, you know, many stakeholders
that when thinking about community level data,
that having more granular levels of geography is important,
because there's a lot of variation,
you know, for example, within counties,
and so this is showing the social vulnerability index
at the tract level,
and, you know, the sort of ranges within
and across counties in Maryland.
And so again, this is the 2018 social vulnerability index,
which is the most recent version of the index
that's currently available.
Next slide, please.
So here, we're providing an example of a linkage
to another dataset,
which is the healthcare cost and utilization project data.
And this is showing that people
from higher social vulnerability areas
are more likely to have an ED
visit than those in lower social vulnerability areas.
So the Y axis is showing emergency department
visit rates.
And if you just look at the left side of the screen,
the overall social vulnerability index quartiles,
you can see that the ED visit rate increases
as the quartile of social vulnerability,
you know, increases from lower to higher social vulnerability.
And you see this pattern across the SVI subdomains as well,
except for the minority status sub domain.
Next slide, please.
So I won't talk through this chart,
but this just really is emphasizing
that a key role of the SDOH database has-
its envision in terms of its use
is to be linked with other data sources.
And so this chart has examples of a number of different sources
that are, you know,
available for linkage at different levels of geography,
which is, you know, sort of readily possible,
given the geographic structure of the SDOH database.
Next slide, please.
And so last, this slide highlights
some examples of potential analyses
using the SDOH database.
We're in preliminary stages of trying to address
the first couple of bullets on the slide,
with HCUP and Medicare claims data.
And beyond this, I just want to note and, you know, again,
it's wonderful to have the opportunity
to be part of this panel,
the tremendous promise of using these different efforts
to collect both social needs and social determinants data,
in analyses jointly.
And so, you know, one of the areas
where I think there's a lot of promise,
but also a lot of, you know,
potential for sort of methodological advances
is thinking through what is the sort of useful
and most impactful role of using community data in context
when the individual level data either does have,
you know, sort of relatively complete social
needs information,
or in case, you know, ranging from that to studies
where there's relatively limited
or no social needs kinds of information,
and, you know, sort of how does one interpret
and best use community level's social determinants
of health data across those different,
you know, sort of use cases,
which I think we are definitely still seeing,
you know, in the space of research in this area.
And so I'll turn to the next slide and basically stop there
and say that we really welcome feedback on the SDOH database.
We're continuing to think about ways to improve it over time.
And I look forward to the questions. Thanks.
Dr. Jenna Norton: Thanks very much, Dr. Keenan,
for that great presentation.
And last but certainly not least,
we have Dr. Scarlett Gomez. Please go ahead.
Dr. Scarlett Lin Gomez: Thank you, Jenna.
I'm going to go ahead and start my slides here.
So, the previous speakers have just set such a nice stage
for what I was going to be talking about,
and I've been following the questions on Slido.
We'll talk a little bit to focus a little bit more
on the direct application of-
can you guys still hear me? I just got-
Dr. Jenna Norton: Your audio went out for a second there.
Maybe try turning off your video just in case-
Dr. Scarlett Lin Gomez: Okay.
Dr. Jenna Norton: -in case it's taking up too much bandwidth.
Thank you.
Dr. Scarlett Lin Gomez: Okay, sorry about that.
How's that? Can you guys hear me?
Dr. Jenna Norton: So far so good.
Dr. Scarlett Lin Gomez: Okay. We'll keep our fingers crossed.
All right. Let me in the interest of time-
So you've seen a number of really great frameworks
for how we should sort of start to sort of move upstream
in terms of our thinking about structural
and social determinants of health.
I really like this particular graphic,
which was put out by the Benton Franklin Health District.
It's really kind of a different way to frame the nice-
Dr. Jenna Norton: Scarlett, I'm so sorry to interrupt.
We're not seeing your slides move.
Dr. Scarlett Lin Gomez: Oh, no. You're not seeing this?
Dr. Jenna Norton: Yeah, maybe if you could unshare
and just reshare again,
sometimes, I have to see this glitch.
Dr. Scarlett Lin Gomez: Okay. Dr. Jenna Norton: Thank you.
Dr. Scarlett Lin Gomez: Are you seeing this?
Dr. Jenna Norton: Yes, now we can, perfect.
Thank you.
Dr. Scarlett Lin Gomez: Okay. Oh, my goodness.
Okay. All right. So we've seen-
we've heard reiterated several times the definition
of social determinants of health from the WHO,
this really- framing this and situating this
within this graphic illustrates the social determinants
of health actually being midstream
in terms of the conditions in which we are born,
or work, live and age.
Where I would like to try to focus this talk
is moving upstream as Dr. Braveman
has nicely started to talk about what are these upstream factors,
what I did was I parsed out the WHO definition
to focus on the part of the definition
that talks about the social determinants of health
as to conditions in which we live in,
and then the structural determinants of health
which are the wider set of forces and systems
that are shaping the conditions of daily life.
I won't focus too much on this because again,
I think that doctors Hooper and Braveman
did just a wonderful job talking about this.
They're really- these are the isms,
the isms according to race, according to class,
immigration status, gender, language, sexual orientation,
these isms that determine the inequitable distribution
of resources
as determined by our institutions.
And so these refer to the policies,
programs and practices within our institutions,
specifically our age, governments,
schools, laws, regulations and businesses.
And these are the upstream factors
that shape the living conditions,
the social determinants of health.
So let's get down to business. How are we measuring this?
Firstly, the social determinants of health,
I want to- we've talked about the PhenX,
social determinants of health toolkit,
I think that is a great start in terms of
if you're kind of just getting started,
a really nice vetted set of off-the-shelf measures to use
for measuring the survey measures at the individual level
and geospatial measures at the contextual level.
I want to now talk about some of the approaches
that our group at UCSF have been taking in terms of measuring
social and structural determinants of health.
Several years ago now, this was before great toolkits
like the AHRQ measures have become available,
before the social- the PhenX toolkit became available,
we became really interested in trying to see,
you know, given the wealth of geospatial data
that are available out there,
can we harness any of that data to better measure social
and built environment measures?
So with that, we developed what we called
at the time California Neighborhoods Data System,
which through funding on several grants
have been since expanded to multiple states.
This is basically a set of curated data,
pulling from existing geospatial sources
for characterizing contextual factors,
these, we focus on date measures
that are available at the level of states.
So these are the states that we've since expanded to.
And we really wanted importantly,
to focus on a small of a geography as possible,
block group level, if possible, if not, then census tract level,
because these are the geographies
that really best represent how individual residents
conceptualize their neighborhood units.
We also have a historical data dating back to since 1990,
and are curating collecting these data
as they become available.
The specifics or the sort of broad domains of measures
that are included in our neighborhood data system,
are measures of social environments,
built environment, physical environment,
and also healthcare contexts.
We've pulled from various data sources
as they are of relevance to the studies that we have funded.
These include, of course, census data,
American Community Survey data, business data,
data from Esri GIS systems, their monitoring data,
street file data for calculating street connectivity,
departments of transportation,
departments of food and agriculture,
and then in California, we have a really unique data resource
that's supported by the Department of Public Health
called Cal EnviroScreen,
which in itself is a curated collection of multiple
physical environmental measures.
I want to talk about another project
that our group was involved
in as a sort of different way to both collect, curate
and disseminate a social determinants of health data
at the sister UCSF health Atlas.
And the URL is here if you'd like to go
and check it out yourself.
The health Atlas is basically an interactive mapping tool
that includes over 100 variables to explore neighborhood levels
social determinants of data,
characteristics to see how they relate to sub selected health
outcomes at a population level. These are the broad,
five broad domains of data measures that we include.
And so one can- the idea for this
was really to be able to make the data accessible
to a broad range of stakeholders,
from researchers,
as well as to community groups and advocacy groups.
And so one can go on and map different domains
that they're interested in,
they can map two domains at a time
to see how they're correlated. You can also download the data.
And many of these measures are available
as small as the census tract area level.
And now I want to shift focus and talk
about the structural determinants of health.
These are again the social inequities,
these isms, according to the minoritized social statuses,
and how they shape institutional inequities.
There have been of late just over the past couple of years
some great papers published and I saw a lot of comments
and questions in the slideshow pertaining to how do we measure,
how does one go about measuring
these upstream factors of structural racism?
This was one great paper from Alson
et al published last year
for suggested measures of structural racism.
I won't go through this in detail,
but I'm going to talk a bit more now
about approaches that our team has used,
this is in collaboration with Dr. Kirsten Beyer
from the Medical College of Wisconsin,
who has developed a rolling novel approach
of using data from the Home Mortgage Disclosure Act, HMDA,
which is mandatory reporting of mortgage application data.
So she's taken these data and she's developed
two measures of structural racism.
First is a measure of redlining.
This is the odds ratio of mortgage loan denial
within a census tract
compared to those outside of the census tract
but within an MSA, and because these are models,
she is able to adjust for a number of characteristics
including sex and the loan to income ratio.
So these are odds ratios. If the OR is greater than one,
it indicates that that neighborhood
or that census tract
is more likely to be denied mortgage applications
compared to its surrounding area.
The second measure that she's developed
using the Home Mortgage Disclosure Act data
is called racial bias in mortgage lending.
This is the odds ratio for denial of a mortgage application
from a black applicant compared to denial of a white applicant.
Again, this is based on model data,
so she, in her model, she has adjusted for sex
and the ratio of loan to income ratio.
Other racial groups are also coded,
so it's possible to calculate racial bias
for any two combination of groups, for example,
Hispanic or Latino applicants compared to white applicants.
And here, an odd ratio greater than one indicates
that that particular census tract or neighborhood
has a greater denial for the minoritized group
compared to white applicants.
Other measures of segregation then,
probably one the folks have seen the most often,
the traditionally used measures of segregation
from the conceptualized and developed by Dun and Massey
based on concentrations of racial or ethnic groups
within a smaller unit,
such as a block group distributed
across the larger units such as an MSA.
So some of these most commonly used measures of segregation
are evenness and dissimilarity,
but there's a number of different measures.
A slightly newer measure,
which has been operationalized by Nancy Krieger
and others is called the index of concentration
at the extremes.
These are at smaller area level measures,
so really measuring localized segregation.
And these compare proportions of two different subgroup
of populations, minoritized populations,
for example, black versus white residents,
high versus low-income, or combinations of these.
We can calculate these for race alone,
SES alone, and race by SES.
And finally, there are different, you know,
all of the measures we've talked about
are static at cross-sectional one point in time,
we can also, of course, conceptualize,
and measure structural racism across over time.
The 1930s Home Owners Lending Corporation
redlining maps were recently released for the US.
And so there have been research, so these are-
we've also included these in our health Atlas.
And research studies have begun to incorporate
these into assessing historical redlining
as they relate to current health inequities.
Gentrification is another way of looking
at changing neighborhoods over time.
And CI has released-
worked with the US Food and Drug Administration
to develop and release measures of persistent poverty
at county and census tract levels.
This is the census tracts that have been in poverty
over a sustained period of decades.
Nancy Krieger and others have looked
at the effects of resonance in Jim Crow
states as they relate to cancer health and mortality.
And then I want to just point to this concept
called spatial justice, which has been-
Baciu et al characterize
this as the relationship between people and places
as mediated by historical, current values,
assumptions, beliefs, policies, investments and practices.
So some of these historical measures
that I've described here
relate to the concept of spatial justice.
And I wanted to spend just a couple last minutes
talking about how we've applied these measures here
into one specific study called the RESPOND Study,
which is looking at prostate cancer
among black African American men.
RESPOND is jointly funded by NCI, NIMHD
and the Prostate Cancer Foundation.
It's led by Chris Heyman at USC. It's funded as a U19,
we proposed it as a program project,
so it comprises four projects.
The first project is led by myself and colleagues at UCSF.
RESPOND really is an integrated set of studies
that's focused on a common theme
of identifying the multi-level determinants
and characteristics of aggressive prostate cancer
among Black and African American men.
This is our conceptual framework,
we find that it helps us
to think about how the different projects
and how the different measures we are collecting
or we should be collecting relate together.
So, our conceptual framework is at the very top upstream,
we have structural racism,
which impacts upon life course social stressors
at the contextual level and at the individual level.
These together over time, over the life course produce stress,
which is embodied in impacts upon biological systems,
which in turn impact upon prostate cancer aggressiveness
and mortality, which is ultimately the outcome
of interest for the RESPOND study.
And then down here, you have, you see an arrow here
pertaining to a germline risk.
So the idea of this is that this pathway here can be moderated
by germline risk,
genetic ancestry and prostate cancer risk loci.
This is how the different projects within RESPOND
are collecting data pertaining to each of the different aspects
of this pathway of this framework
and then how they all relate together.
So I want to focus a bit more now on project one,
which is the project that we're leading at UCSF.
We, within RESPOND,
we are collecting data from multiple sources.
Firstly, we are identifying and recruiting men
based on state cancer registry.
So these are African American or black men
recently diagnosed with prostate cancer,
we reach out to them, ask them to participate in the study,
and they are asked to fill out a survey
which assesses the factors that you see here.
We included measures of life course
multi-level social stressors,
and so we really try to make a point here to adapt questions,
survey instruments that have already-
that are validated and or widely used,
so that we recognize that our- I should have mentioned,
our goal is to recruit 10,000 African American men
with prostate cancer nationwide,
we're recruiting from six state cancer registries.
So recognizing that this cohort of 10,000 African American men
with prostate cancer can really serve as a valuable resource
for comparison to- for additional research,
we really wanted to be sure to collect data
using measures that are well validated.
So we've adapted the Williams lifetime
everyday discrimination scale
just as an example to ask about current experiences
with discrimination as well as several other life periods.
We also, for example,
adapted the CDC adverse childhood experiences scale
to ask about adverse life experiences
over different life periods.
We've included Dr. Beyer's structural racism measures,
so through geocoding both participants' addresses
at time of diagnosis,
as well as ascertaining a residential history,
were able to access and ascertain these contextual
structural social determinants measures that you see here,
not only at the time of diagnosis,
but over their life course. And so the- you see the measures
that we're looking at racial ethnic segregation, composition,
social inbuilt environmental factors.
So we have a whole lot of data
that we are collecting pertaining to structural
and social determinants of health,
within this RESPOND study,
we have data across the life course,
we have data at multiple levels,
and we have data from multiple dimensions,
from self report, as well as from geospatial sources.
Our approach or plan is supply mixture modeling
to be able to combine these different domains
and dimensions together,
and then to capture to be able to have-
measure latent measures that capture each life period
for contextual level stressors
and individual level social stressors,
and then to develop trajectory models
so that we can measure how these stressors
at the contextual level and individual level
change over the life course.
So I want to emphasize and with the slide that,
as I'm sure you've appreciated from the talks
that we've heard so far is that these approaches
really requires a team science approach.
And I think, foremost, and most importantly,
we really need to include collaborators
who bring expertise in social sciences
as well as social epidemiology.
I think increasingly understanding the extent
to which these upstream factors impact upon biology,
we need to be able to work together
with genetic and molecular epidemiologists.
Obviously, we're talking-
oftentimes, we're talking about big data,
data sciences and bioinformatics become very important.
Biostatisticians, and to the extent that we also
are able to leverage existing geospatial data
and being able to work with environmental
and GIS epidemiologists, demographers,
it's also critically important. And most importantly, I think,
is working with communities and understanding it's nice
and all we have all of this data,
we as epidemiologists
and scientists can put our hats on and propose hypotheses,
but really listening to the life experiences
and stories of communities
to really understand the relevance
of what we should be studying, and what then helped us to study
is a really key part of this team science approach.
Thank you.
Dr. Jenna Norton: Thank you, Dr. Gomez.
That was an excellent talk.
I can only assume you have a crystal ball,
since your talk nicely addressed some of the questions
that came up in the chat during the prior discussions.
So thank you.
So I'd like to start off our discussion
with a question or sort of two comments and questions
that I think get at the heart of this panel.
So one comment pointing out the distinction
between population level social determinants of health,
and how they have different uses
than patient reported data at the individual level.
And a question that was addressed to Dr. Keenan,
but I think the panel all may wish to comment on
is sort of since the AHRQ database
and some of these other area level measures
were developed for the whole community,
to what extent can they be used as a proxy
when you're studying subpopulations
within the community, for example,
people with a specific disease, or other specific concern?
Dr. Patricia Keenan: I'm happy to start.
What I would say is that the community level
measures of SDOH, including the indices,
I think of them as capturing the community context.
And so, you know, for patients of a particular population,
if it's possible to link the community level data
to patient level information, then it would tell you about,
you know, characteristics of the communities in which,
you know, the population of interest lives.
I think that is different,
and as also pointed out in the comments,
that that's not the same as telling you,
you know, what the social needs circumstances
at a personal level are of a given population.
And I think that is a really important distinction.
And I was trying to sort of allude to that
in my concluding remarks,
because I think there is still a lot of more work
that could be done to help think through
and, you know, sort of have more, you know,
sort of a larger body of work, talking about, you know,
sort of how to use community level SDOH data,
and interpret it when you do
and don't have the individual level social needs information.
But again, I would, just as in sort of overall point,
emphasize, as was noted in some of the comments,
that community level and personal level information
definitely are not measuring exactly the same thing.
And so, you know, when used with
or without patient level social needs,
it also might be important to think
through how to interpret then,
you know, sort of the meaning of the community level of factors.
Dr. Jenna Norton: Dr. Gomez,
anything to add from your perspective?
Dr. Scarlett Lin Gomez: I don't.
I think that was just an excellent response,
I don't have anything further to add.
Dr. Jenna Norton: Wonderful. All right,
so another theme that I've seen in a couple questions here
is this idea of ethical concerns or other challenges,
you know, particularly if you're collecting information
on something sensitive like experiences of racism,
and integrating that into the electronic health record
and how that might be stigmatizing.
So Evelyn, I saw you already put a link into the chat
to begin to address that question,
but if you have further comments,
or Dr. Keenan or Dr. Gomez, if you have comments,
please feel free to add those as well.
Evelyn Gallego: Sure, I just- Yes, I put a link
because this comes up often in our discussions
when we first started the project,
it was always around, you know, how is this going to address
furthering health disparities, right, and discrimination?
And I do think we-
so we developed the data principles,
but knowing that, you know, there's still more work,
I think all what you are doing and the interest here is,
we need to be able to collect this type of data,
acknowledging that there are what I'd like-
you know, refer to unintended consequences,
but, you know, how can we do better with guiding principles?
I think more evidence is needed, but it's a risk overall.
I also do think the Gravity-
I will acknowledge the Gravity Project,
we are not directly working on standards related to consent,
that are being developed by other entities.
But I think that's also an area where we ensure that we are
gathering the individuals' consent to capture,
share and exchange the data. And it becomes truly important
when you have sensitive information
as we look at domains around violence and axes
where, you know, that data wants to be-
the individual wants to hold it close.
And if the other panelists want to chime in-
Female Speaker: I just want to add
that I think this is, you know, as epidemiologists,
we always want more data, better data and more data.
And I've been pushing a long time for standardized
and collection of social determinants of health
across healthcare systems, because we are increasingly
really relying a lot on that data for research,
but also, of course, for healthcare systems improvements.
But at the same time, I think, to Dr. Gallego's point,
we do have to be careful. And I know, for example,
in some California public institutions,
we explicitly do not collect data on,
for example, immigration status, because we-
even though we ourselves know that,
you know, we will not use it for wrong,
but that doesn't-
having there and putting patients in the position
of having to disclose that is not something
we as a healthcare institution
want to be in a position of doing.
So I think that there is definitely a balance
and a trade-off.
Dr. Jenna Norton: Excellent, thank you.
So another question.
So I'm going to combine two sort of different
but similar questions.
So one is this idea of, you know,
how do you collect information on these issues
for people who have stigmatized diseases,
who may not feel comfortable providing,
you know, all of their previous addresses,
or who might not want to answer extensive surveys
about discrimination or childhood experiences?
And then even for folks who may feel comfortable
providing these data, their addresses change over time,
and expecting people to recall all of these data over time
can be a challenge.
And so how do we deal with some of those issues?
Evelyn Gallego: That is a hard one, Jenna.
Even I think it's not only recollection,
we have- we see this even with clinical data,
you know, it's just even how do you,
like, data provenance, you ensure that that individual
is giving the right data, identity management,
all the things that need to carry
even before we get into we say demographic type information,
I think it's hard to track, I do see value
in a lot of the Health Information Exchange systems,
that can be the ones that help health systems
match for patient matching,
I think there's a significant opportunity there.
But, you know, as a patient and consumer myself,
you know, you do get frustrated
answering the same question all the time,
and you wish that there was a way
that it could auto populate with existing data.
But I do think that this is a common problem. Overall.
Female Speaker: I can speak to that a little bit.
I think there were two questions there.
First is challenge of asking some of these very sensitive
and potentially stigmatizing questions,
and we've definitely faced and we still continue to face that.
And I think one approach that really the Adverse Childhood
Experiences scale, for example, that we're using does have some
very, very sensitive items within it.
One approach that we have found
is making sure that our recruiters,
those who are interfacing directly with the patients
and the community members that they're recruiting,
can identify with those communities and patients
and having them be able to speak to I get it,
I know where you're coming from.
I know your concern. This is, you know-
I think this is why these questions are important,
and as researchers,
we've talked a lot too directly to these recruiters,
we've written a lot of FAQs for them to use,
that they have available at their tooltips
to be able to address these questions as they come up.
But I think that's really critical.
The second question was regarding recall
of residential history,
and yes, there's an "I can't even recall
were the last three addresses where I've lived at."
The approach we're using is actually through linkage
to public records databases,
to be able to ascertain residential history.
This is a method that was tested by Westat
in collaboration with NCI,
and there's a white paper that's been written about it.
So that's the approach we're using,
because we recognize that it is nearly impossible
to have people recall
their exact residential history over time.
Dr. Jenna Norton: Thank you. All right.
So in our last minute, I'm going to ask one last question.
There are so many good questions in the chat,
I'm sorry, I won't be able to raise them all to the panel.
But, so I wanted to bring up-
so there's a trend to use population level data
to target or triage person level interventions.
I'm wondering if the panel can give their thoughts
on this trend and its likelihood of reinforcing inequities.
Sorry, it's a little bit of a tough one,
and I welcome any comments or thoughts
from the panel on this one.
Dr. Patricia Keenan: Very briefly,
we've talked with some health systems
about how they use social determinants
and social needs data.
And we have heard of instances of systems
using a combination of social needs and community levels'
social determinants data to kind of do predictive modeling
to try to identify patients who, you know, might be at risk
for an adverse outcome down the road,
and then intervene. And so that's an example
of a use of the SDOH community level,
as well as social needs data.
The likelihood of, you know, sort of reinforcing inequities,
I might see if other panelists have comments on that,
you know, I think there's, you know,
can be possibilities of using data,
you know, kind of in ways that are intended and unintended.
And this is certainly, you know,
I'm sure not immune to that risk.
Evelyn Gallego: Thank you, Patricia,
I would just add, you know, our work is just starting.
So I- you know, for our intent is the use, you know,
our mission is really around using this type of data.
So that's, we're excited about the pilots,
where we can learn from entities
that are gathering individual level data
and seeing how they use it.
But follow Patricia's point around
that we've, you know, the health systems
and the payers are using a combination of this data,
they're most excited about the standards,
because they have to do the mapping,
you know, it's not all the same level of data,
and they just want to be able to have structure and standards
on how the data is represented
across different settings of care.
So I think there's an opportunity,
but again, there is that risk, too,
is we don't want to use the data to further health disparities.
And we're really at the start, right?
I always say to everyone, the standards is like we created
the dictionary with definitions,
and now everyone has to write and tell their story,
and we need to see what comes back.
Dr. Jenna Norton: All right, thank you.
So we're a little bit over time,
but we've cut into people's lunch,
so I apologize for that.
So we will be starting lunch from 1:00 till 1:45.
So please be back at 1:45 for the continuation of the meeting.
Thank you to our panelists again,
for great presentations and thoughtful responses
to these questions.
And, you know, there are many questions
we didn't get to in the chat and I think we will do our best
to try to find other ways of addressing those as possible.
Thanks, everybody, and enjoy your lunch break.
Dr. Sharon Jackson: Good afternoon, everyone.
Can everyone see me?
Dr. Jenna Norton: Yes.
Dr. Sharon Jackson: Great. Good afternoon, everyone.
Thanks for returning after lunch.
Hopefully you had a good quick break.
I'd like to introduce myself. My name is Dr. Sharon Jackson.
I'm with the National Institute on Minority Health
and Health Disparities.
And we're very excited this afternoon
to start our case studies sessions.
We're going to start this afternoon
with our basic science and clinical science case studies.
And we have the privilege of having two great panelists:
Dr. James Collins from Northwestern University.
He is a professor of pediatrics
at Northwestern University Feinberg School of Medicine,
and a medical director of the neonatal intensive care unit,
and the associate director of the pediatric residency program
at Ann & Robert H. Lurie Children's Hospital of Chicago.
And our panelists after Dr. Collins
will speak on basic science, and that's Dr. Jenny Tung.
Dr. Tung is a professor of evolutionary anthropology
and biology at Duke University,
and the director of the Department
of primate behavior and evolution
at the Max Planck Institute for Evolutionary Anthropology.
She is also an affiliate
of the Duke Population Research Institute,
and the Duke Center for the Study of Aging
and Human Development.
So with those introductions, Dr. Collins, take it away.
And thank you.
Dr. James Collins: Here we go.
Good afternoon, everybody. I trust that you can hear me.
I appreciate the opportunity to share with you my perception
on a long-standing issue in this country,
namely the racial disparity and adverse birth outcome.
I'm going to try to set things up
by really putting the spotlight on structural racism,
and show you how the social determinant of health
is an important contributor to our adverse birth outcomes.
But first, I'd like to set the stage-
see here, advance the slide.
Looks like my slides are frozen. Got to love technology.
Let's try this here. Here we go.
Set the stage by just kind of letting you know
how we do overall with respect to infant mortality rates,
which are defined as the number of deaths
which occur in the first year of life.
A high rate signifies unmet health needs.
As a country, we ranked neither first,
fifth, 10th, 15th, or 20th.
Actually, we're 27th in the world infant mortality rates,
and our position has deteriorated since 1960.
This slide shows infant mortality rates
by race in this country.
And we see that African American rates are very high,
essentially twice that of non-LatinX whites.
This is largely because birth weight and gestational age
are the primary determinants of birth outcome,
and African Americans have a 50
to 60% higher rate of being born prematurely at 37 weeks.
This slide shows this disparity exists
among women who have 12 or more years education.
The X axis is maternal education in years,
the Y axis is preterm birth rates.
Whites are in red, African Americans in white.
And we see for both races,
as education or something closely related to it improves,
preterm birth rates decline.
But even among those who are college educated,
we see that the disparity persists.
Transgenerational factors are defined as those factors,
conditions, environments expressed by one generation
that relate to the pregnancy outcome of the next generation.
We see that a greater percentage of African American
compared to white women were themselves born preterm.
We see African Americans in light blue, whites in red,
have a higher rate of themselves being born less than 30 weeks,
30 to 33 weeks,
and late preterm at 34 to 36 weeks.
This is important because we know
that maternal gestational age
is also associated with infant gestational age.
The X axis is maternal gestational age,
as it increases, the rate of preterm birth decreases.
But we see that the disparity persists
even among those women who born at term.
Slavery is the ultimate transgenerational process
for African Americans.
We know that approximately 400 years African Americans
have been in this country,
approximately two thirds was spent in slavery.
This created a slave health deficit.
The next 100 years,
essentially no citizenship rights were received.
Therefore, the slave health deficit was uncorrected.
It's really only been in my lifetime
that we received citizenship rights,
but clearly, it's been a struggle.
One of the most interesting things
is that women who themselves were born in Africa
or the Caribbean, who come to United States,
actually have birth outcomes very similar
to the general white population.
However, their daughters who grew up in this country,
one generation later, their birth outcome plummets
and actually approximates that of US-born black women.
So we conclude that race is not a biological construct
that reflects innate differences,
but a social construct
that precisely captures the impacts of racism.
This social construct also exists
among the LatinX population.
This slide shows infant mortality rate of LatinX infants
born in this country over a two-year period.
And we see for those women who define as black,
we see that they have a higher infant mortality rate
than those who are white, but only among the US-born,
only among those who have a lifelong residency
in the United States. Among foreign-born black women,
we do not see a disparity based on race.
So thus understanding the US-born
black women's pregnancy disadvantage
requires an analysis of the effects of race
as a social construct,
thus addressing racism in its various forms.
Hatred has affected the heart of America,
this heart is spreading into our intellect,
our economics, our politics, the result of this disease:
the death of George Floyd in Minnesota,
the shooting death of an unarmed jogger in Georgia,
the false accusation of a birdwatcher in New York City.
This was written by a columnist in Atlantic News
about two years ago. We've thought to ask a question,
do states with more killings of unarmed black people
have larger racial disparities and preterm birth?
We identified police killings from unarmed black people
from the 2013 to 2018 Mapping Police Violence database.
It included over 308 killings of unarmed black people.
The lowest quartile was states that had no killings,
remaining states were divided into quartiles one through four.
We extracted data from the CDC
with respect to birth records from non-LatinX white,
non-LatinX African American women.
This slide requires a little bit of concentration.
For this dataset, which included 2018 births,
the median preterm birth weight disparity was 1.5.
We see that states in the zero quartile,
those in light green or green,
tend to be to the left of this, or less than 1.5.
For those in the first quartile, the orange's group,
were also less than the median.
If we compare those groups who are in quartiles two
through four, this group here, these rates actually do exceed
these rates here statistically speaking.
From a statistical point of view,
it shows that states with more killings of unarmed black people
do indeed have larger black-white disparities
in preterm birth.
We speculated that the discriminatory behavior
of police officers
reflects structural racism and state culture,
which serve as upstream causes of health inequities
due to differential access to health promoting environments.
Redlining. Redlining is the practice of denying
or charging more for services such as insurance,
banking, access to healthcare or employment to residents
in often racially-determined areas.
The term refers to the practice of actually taking a red line
and making it by a map,
to just delineate where banks would not invest.
This is not something that happened back in the day,
we looked at data from Chicago,
looking at health, mortgage discrimination rates
and found that a redlining index here on the X axis
was associated with preterm birth rates
among African Americans.
Spatial social polarization, over the last several decades,
American neighborhoods have undergone a phenomenon
called spatial social polarization.
It is defined as the segregation within a society
that emerges from income inequality displacements,
resulting in differential groups from high to low.
Neighborhoods of extreme wealth and extreme poverty
have grown exponentially,
kind of squeezing the size of middle-income neighborhoods.
Not surprisingly, this change has not been distributed
evenly by race.
This slide here kind of explains this for Chicago,
we see in 1970, very high-income neighborhoods
were pretty neutral right around the lakeshore.
Very low-income neighborhoods
were concentrated South Side and near west side.
The vast majority of Chicago was middle-income.
However, we see over the next preceding 50 years
or so that the red has grown exponentially as we said,
as has the blue and that middle-income group
has been squeezed. What does this mean?
Is that the lifetime neighborhood experiences
in Chicago differ based on race.
We see that most, 84% of whites have a lifelong residence
in high-income neighborhoods.
Only about 2% of African Americans
live in such favorable neighborhoods
throughout their life course.
In stark contrast, nearly 80% of African Americans
have lifelong residence in low-income neighborhoods.
Only 2% of whites live in those unfavorable neighborhoods.
Not surprising, lifelong residents
of low-income neighborhoods in white
is associated with the increased risk
of low-birth-weight compared to lifelong residents
in high-income neighborhoods.
And this association tends to be the same
among African Americans and whites,
maybe even a little stronger among whites.
The population attributable risk
takes into account the relative risk,
but also takes into account the prevalence.
Because the prevalence of low-income neighborhoods
is so much higher for African Americans,
the population attributable risk of neighborhood poverty
is so much greater for African Americans.
If we were to get rid of poverty,
we would get rid of less than 5%
of low-birth-weight white infants,
less than 2% of preterm infants and less than 3% of SGA infants.
In contrast, if we got rid of poverty for African Americans,
we would get rid of 25% of low-birth-weight infants,
about 12% of preterm infants,
about 26% of those with a birth weight
less than 10 percentile for gestational age.
We ask the question, to what extent
is African American women's upward economic mobility
across the life-course associated with birth outcome?
We found that upward economic mobility
from early life residents in poor neighborhoods
is associated with a lower preterm birth rate
for African American in Cook County.
This Y axis is preterm birth weight.
We see for African Americans lifelong residents
in low-income neighborhoods,
that's less than the- in the first quartile,
meaningful income less than 20 grand a year,
that preterm birth rate is very elevated.
For those who went from the first quartile
to the second quartile,
preterm birth rates came down a little more.
For those who went from the first to the third,
things came down even more.
For those who had some significant upward mobility
from the first quartile to the fourth quartile,
this is meaningful income in excess of six figures,
you see that preterm birth rates were quite lower.
However, we found that moms are themselves low-birth-weight.
In this case, low-birth-weight is in white,
those who are not low-birth-weight
were in red, maternal economic mobility is on the X axis.
We see that in the red group,
preterm birth rates decline as upward mobility occurs.
For those who moms who were born poor and born small,
these birth rates hovered in the elevated category,
and that did not decline with upward mobility.
We did some fancy statistical modeling,
and we found that for moms who are aged 20 to 30
and not low-birth-weight, low, modest and high upward mobility
is associated with a decrease odds
of delivering a preterm infant.
However, for those moms who were born small themselves,
these odds ratios approximate at one,
and these [INAUDIBLE]
also included unity, showing that upward mobility
was not associated with decreased risk of preterm birth.
This slide we looked at another outcome measure,
this time small for gestational age,
birth weight less than a 10th percentile,
and we see a very similar trend.
Rates are very high for those of no upward mobility,
and things come down with upward mobility.
We do our same fancy modeling again.
And for those who had some low, modest or high upward mobility,
these odds ratios were significant.
But for those who were themselves born small,
these odds ratios were not significant.
We hypothesize that this reflects fetal programming.
Fetal programming acts at the level of DNA
in a phenomena called epigenetics.
Researchers have used low birth weight
as the major marker of aberrant fetal programming.
We suspect that aspects of mother's social environment
subjects her to influences during fetal life
that results in her slowed growth in utero,
but also programs her to have a preterm
or SGA infant as an adult.
Clearly, more research needs to be done
to focus on this hypothesis.
Still looking upstream, structural racism,
the socio-economic and cultural landscape
of African American fatherhood
suggests that the role of father involvement
and socioeconomic position may be particularly salient
to the racially disparity and birth outcome.
We know the college graduation rates for African American men
are very low compared to white men.
We know the unemployment rates are sky high,
we know that incarceration rates are also extremely elevated.
This is another side that requires
a little bit of concentration and I apologize.
But if we look here on the X axis,
this looks at the disparity with a difference
in preterm birth rates
between US-born in foreign-born women,
and we see that that disparity was 3.3.
The gray represents what is unexplained.
This is in one year worth of birth rate records.
Clearly structural racism isn't measured on the birth record.
So we can't really-
it's not surprising that this is unexplained.
There are other variables we couldn't measure.
We see that's light blue, prenatal care
and to pregnancy integral,
that light blue was palpable group.
The red group is the maternal education.
That's the explained, that maternal education
explained to disparity.
This darker blue is paternal involvement,
which is very weakly defined as being listed
on the birth certificate.
It seems to explain a greater part
of the disparity than maternal education.
If we look at the difference in preterm birth
between US-born and foreign-born white women
with preterm delivery, we see a very similar trend.
Namely, we know some groups are explained,
something is explained by prenatal care,
cigarette smoke, et cetera.
We know that maternal education explains the palpable group,
but what surprised us again, paternal involved involvement
seems to be explaining
such a significant proportion of this disparity.
This slide looks at the excess preterm birth rate
among US-born compared to foreign-born women,
those who have the role of dad's education.
The X axis is Dad's education this time,
Y axis continues to be preterm birth.
We see among US-born black women in blue
and foreign-born black women in gray,
we see that both groups' rates decline
as dad's education improves.
Using Oaxaca analysis,
which takes into account a contribution of a factor
with respect to other factors, we found that dad's education
as reported by mom on the birth certificate
explained a greater proportion of the disparity
between US-born and foreign-born black women in mom's education.
One of the weaknesses of this study
is that dad's education is reported by mom.
So we did a study looking at something more objective,
namely dad's neighborhood income
when he was born and dad's neighborhood income
when he delivered his child, when the baby was delivered.
And we see that for dads
that have lifelong low socioeconomic position here,
first year mortality was very elevated.
Probably as dad's socio-economic position
improves, rates decline, since the dads who were-
infants who are born to dads with a lifetime high
had a low infant mortality rate of 4.9 per 1,000.
This slide kind of summarizes
looking at early preterm birth rates
by a paternal acknowledgement and socioeconomic position.
And we see among infants born to African American moms,
those who had not acknowledged fathers
had a high rate of really preterm birth.
For those who had acknowledged fathers,
for those fathers of low socioeconomic position,
things came down a little bit more, 4.3 per 1,000%.
But for those who had a lifelong-
who were born to acknowledge dads for lifetime high,
that rate was down to 3.29.
Interesting, if you look at the ends for the city of Chicago,
which is the vast majority of Cook County,
we see that very small percentage
actually had acknowledged dads of life long high.
A same trend occurred among non-LatinX white mothers,
namely, for those who are not acknowledged,
early preterm birth rates were really elevated,
and then things marched their way down.
But also notice that a vast majority of these women
actually had dads who had high acknowledged,
were acknowledged and had high socioeconomic position.
Again, this suggests a role for dads that's independent of moms,
but clearly, this is an area of further research,
which would be important to delineate.
Another form of racism is personally-mediated,
a little bit easier to measure in some respect.
And we performed a study where we actually interviewed moms
who delivered preterm infants,
and we interviewed another group of moms
who delivered term infants,
these women were African American.
And we found that a greater percentage of those
who delivered preterm infants
were exposure to racism in three or more domains:
at work, getting work, in the social, in the real world,
compared to African American women
who delivered term infants.
This study was relatively small by EPI standards,
and it was retrospective. Since this study,
there have probably been over two dozen studies
which have expanded this and confirmed these findings.
The strongest is actually I believe,
by Paula Braveman et al and her group in California,
where they actually administered surveys
to over 2,000 African American women,
over 8,000 non-LatinX white women
who just have singleton term births
between 2011 and 2014. And in their study sample,
approximately 37% of African Americans
and 6% of white women
reported chronic worry about racial discrimination.
The investigators found that chronic worry
about racial discrimination
was associated with preterm birth
among African Americans, with the relative risk of 2.0.
Very interesting, the racial disparity in preterm birth
was attenuated and actually it became non-significant,
when adjusted for chronic worry,
relative risk went from 1.6 to 1.3, again,
suggesting that chronic worry for racial discrimination
is an important contributor to the racial disparity
and adverse birth outcome.
So I've given you a lot of information
in a short period of time.
And I want us to put on a public health path to say,
well, what challenges do we face in eliminating disparities?
One, I think, is a challenge- is that historical insults
contribute to current disparities.
Until the effects of past historical ills are undone,
disparities will not be eliminated.
Two, social factors
are the largest contributor to disparities,
but often ignored, medical, molecular and biological factors
get the most focus and funding.
Two, we do not consistently distinguish
between healthcare disparity and health disparity.
Differences in medical care is only one of many contributors
to the overall health disparities.
So if we're going to improve pregnancy and birth outcomes,
I think we have to go big, we have to go ecological,
we have to begin to address
the social and economic inequalities
that are the root causes of health disparities.
Our task should include putting structural racism
on the agenda, namely, naming it as a force determining
the distribution of other social determinants of health,
but also to routinely monitor for differential exposures,
opportunities and outcomes by race.
Thank you very much for your attention.
I'd like to hand the baton back.
And then hopefully, we'll have some time
possibly for some questions before the session ends.
Dr. Sharon Jackson: Absolutely. Thank you so much, Dr. Collins.
That was a fantastic talk. Quite thought-provoking.
And thank you for also making the segue to Dr. Tung
and we will have time at the end for a few questions
as well as in the breakout session later on.
So with that, Dr. Tung, if you're ready,
we look forward to your presentation.
Dr. Jenny Tung: Okay. Can you guys hear me all right?
Dr. Sharon Jackson: Yes.
Dr. Jenny Tung: Good. Okay.
Thank you very much to all of you for involving me
in this really fascinating workshop.
I'm going to switch gears a little bit
and talk not about humans,
but about our close relatives, the non-human primates.
Because I know, that's probably a minority interest
of the folks involved here,
I'll go ahead and start by just giving you an idea
about what one aspect of sociality
and social relationships
can look like in the nonhuman primates that we study.
So what I'm showing you here is a picture of two wild baboons.
This is a female named Rwanda, who my collaborators
and I have studied continuously from her birth in 2002,
to the present. And in this picture,
she happens to be engaging in affiliative social behavior,
the type of behavior that creates
and maintains social relationships
with her then adolescent daughter, Rodeo.
I'm showing this picture of Rwanda in part
because she is an example
of a particularly privileged member of baboon society.
In the social hierarchies that define baboon society,
she has been at the top or near the top through her entire life.
This has likely paid some dividends for her
in terms of measures of Darwinian fitness
or Darwinian success. She's had 10 live births so far,
three fetal losses, three miscarriages,
with her most recent birth a son last year in 2021.
I mentioned that this may have something to do
with her relative social privilege.
We know that high-ranking high social status mothers
in the society that I'm showing you here,
tend to reach maturation earlier,
tend to start their reproductive careers a little bit earlier,
and probably because they have better access to food
and other types of resources,
they also have shorter inner birth intervals.
They recover from postpartum amenorrhea earlier.
A number of Rwanda's offspring have been females like Rodeo,
and this likely confers another benefit to her.
Female kin are female baboon's closest social partners.
And we know that females
who are the most integrated into their groups
versus the most isolated in their groups experience
quite a large difference in their predicted adult survival.
So here I'm showing you the top quartile
versus the bottom quartile of the most socially integrated
versus socially isolated baboon females
and the difference is in life-
a difference in life expectancy of about two to three years.
Daughters like Rodeo
also experience relative social advantage.
And this is even in even larger effect sizes,
it's actually the largest effect size we know of on survival
in this particular population of primates.
What this picture is showing you is the difference in expected,
again, adult life expectancy for those individuals like rodeo
who were born to high social status mothers,
to socially connected mothers, to mothers that manage to live
throughout their entire juvenile periods.
And those are the sort of silver spoon females
that we show in dark blue,
who are expected to live a whopping 10 years,
about a decade longer than those females
who may live in the same group,
but experience three or more major sources
of social disadvantage early in life,
pointing again to the very large importance
of early-life experience in the lives of these animals,
similar to what's been described in human societies.
So I showed you a very granular example here,
but I want to point out the increasing amounts of work
in not only humans, but in other social mammals,
show that aspects of the social environment,
particularly things like social connectedness,
versus social isolation and social support,
have quite substantial effects on how long individuals live,
on their health and their survival.
So this is from a review some colleagues
and I wrote a few years ago,
looking at the data available at that time
on all cause mortality in natural populations,
that is populations outside captivity, of social mammals.
And what you can see is, humans are not alone.
Individuals who are more socially integrated
into their societies have more social support,
live longer natural lifespans.
And so this seems to be a general phenomenon
that is a major driver of the type of work
that my group does, which seeks to understand
why social relationships both in early life and adulthood
have such remarkably large effects on life outcomes.
These are the four species that my lab
has studied in the last year, a few years.
I'll focus today on the non-human primates
and talk briefly about the reasons
why studying these non-human animal models
can be useful in understanding
the social determinants of health in humans.
The first, of course,
is the relative simplicity of their society.
That's what makes animal models useful
in any aspect of the Biomedical Sciences.
Unlike in humans, it's relatively straightforward
to measure social bond strength and social status,
because all the social relationships
that these animals experience are constrained
to the social groups in which they live.
This actually makes them a somewhat poor model
for some of the larger scale structural sources of inequity
that have been talked about in this workshop,
but relatively good for trying to isolate the effects
of immediate individual differences in social support,
for instance, or in social status,
because social status tends to be pretty one
dimensional in these groups.
Additionally, while non-human primates live a long time
compared to classical animal models,
like fruit flies, or mice, or C.
elegans, they live shorter lifespans than humans do.
And a common method of collecting behavioral
and social data on them is to actually collect data
in real-time through direct observations,
which we can do in some cases on a near daily basis
from cradle to grave.
So what I'm showing you here, for example,
is from that baboon population I talked about earlier,
in which observations began in 1971.
This shows pedigree relationships,
from the first generation until where we are today,
which now is just cracking the ninth generation
of continuous data observation
on full lifespans of these animals.
And of course, in some settings, and this is a setting
I'll focus on for the rest of my presentation today,
it's possible to have some experimental control
over the social environment in a way that is both logistically
and ethically unfeasible in humans.
So I'll focus here, particularly on social status,
social rank in nonhuman primates,
which Robert Sapolsky and others have argued
is the closest approximation that we might be able
to make to differences in socio-economic status in humans,
because like socio-economic status,
differences in social rank control access
to resources in a systematic fashion
that can be quite stable over time,
and can be regularly enforced through harassment.
So here, I'll focus on the rhesus macaques,
that's the species shown here on the right,
because in rhesus macaques,
that is, in fact, the case in females,
that high status females regularly enforce
their rank through harassment and sort of ritualized behavior
that reinforces differences in the hierarchy,
and that low status individuals
may experience throughout their lifespans.
Crucially, most of this behavior is not by a physical contact,
but rather by displacement behavior,
by threat behavior, and so on.
I said that it's possible to study social status
and the long-term effects of differences
in social status experimentally in this system.
And that's because in female Rhesus,
we can manipulate social status over the long-term by
altering how females are housed together.
It turns out that when you introduce females
who had no prior social history with each other
into a new social group,
females who are introduced earlier
become high status in those groups.
And as you sequentially introduce females
into the group afterwards, they tend to occupy the space
in the hierarchy directly below the females
who already exist in those groups.
So we can randomize order of introduction,
and in doing so,
we can effectively randomize social status,
and those social status hierarchies
can remain stable for years.
So we've formed these five-member social groups.
And then we can replicate that group formation
across replicate social groups,
we typically work with about nine
or 10 different social groups.
To really pin down causal relationships
between the stress of social subordination
and downstream outcomes,
we can then do a secondary manipulation,
we usually do so about a year later,
where we take the females
who are top-ranking in those groups
and put them together into a new social group,
we take second-ranking females and put them together,
third-ranking, and so on down the hierarchy.
Because these females naturally will reform
the social hierarchy, they rapidly do so,
allowing us to watch what happens to females
who move from the highest level in their society
to the lowest level,
from the lowest level to the highest,
or who maintain high versus low social status
throughout the course of our experiments.
This is a picture that gives you an idea of how this works.
On the Y axis is a quantitative measure of social status,
high-high on the Y axis, low-low on the Y axis.
And this shows the process of group formation
for one of our social groups that we worked
with quite some time ago now, about a decade ago now.
Each line shows a different female,
and you can see sequential order of introduction
when they appear in the group, we introduce them into the group
and they very rapidly form a clearly delineated social status
hierarchy that again remain stable
throughout the course of the experiment.
So we've used this experimental paradigm
now to ask a lot of different questions
about what happens across the two phases of these studies,
the females, followed by rank rearrangement,
and then the same females later on
when they occupy different places
in the social status hierarchy.
We know now that we can manipulate social status,
and those manipulations provide evidence
that the chronic stress of subordination,
and again, I should say,
controlling for healthcare access,
controlling for access to food, controlling for shelter,
controlling for demographic structure,
and so on, all of those are the same
for all of these individuals,
has strong effects on behavioral traits,
including ones associated with relative baldness,
or relative anxiety in these groups,
that it influences physiology through influencing processes
associated with glucocorticoid negative feedback,
that it changes aspects of the contents of cells
through affecting mitochondrial DNA copy number.
And then it affects how the genome is actually regulated,
how much, when and where of a gene product
is made through influencing gene
expression in a cell-type dependent manner.
Additionally, we have some evidence that differences
in the social environment itself
can affect how the physical structure of the DNA
folds within the cell.
And I'll focus here particularly on some of our data,
showing that differences in social status influence
the ability of immune cells to respond to models of infection.
In this case, when we culture blood
from each of these individuals in tubes
that contain cell culture media only, basically,
the food that cells need to survive outside the body
for some time, or cell culture media,
in parallel, cell culture media plus another additive,
in this case, Lipopolysaccharide,
which is a component of the gram negative
bacterial cell wall and models infection
by a gram negative bacteria.
Okay. What I'll show you here is work
that was led by a former postdoc of mine,
Noah Snyder Mackler, and a postdoc of my collaborator,
Luis Pereira, Joaquín Sanz.
What they did was look at the levels of gene activity,
the levels of gene expression in two samples
collected from each of 45 individuals
that we had randomized into high versus middle
versus low social status.
So here we measure the activity of about 9,000 genes
in the genome for two samples for each of those individuals.
And I'm just sort of squishing down all of that data
into two dimensions here using a principal components analysis.
So each dot here represents a different sample.
The first principal component of variation
in the overall sample maps on
to whether that sample was unstimulated,
was a control sample in blue,
versus whether it was exposed to LPS.
And you can see a big separation between samples
that think they're being attacked by bacteria
versus those that aren't, that's exactly what we'd expect.
And it's what we would expect in our own cells
if we repeated this experiment, for example,
by drawing blood from humans.
What's perhaps more interesting and more relevant
to this audience, though,
is the second major access of variation in the sample,
the second principal component is very, very strongly
correlated with the social status of the individual
who donated that sample.
We can look at this in an even more granular fashion.
So now, I've switched from each dot in this plot,
and a plot being a sample, to each dot being a single gene
that we were able to measure in this experiment.
And what I'm showing you is the effect of dominance
rank of social status
on gene expression in the control condition on the X axis,
versus the effect of dominance rank social status
on gene expression in the same individuals
when their cells are stimulated
by this model of bacterial infection.
And what you can see is an overall positive correlation.
So in other words, if social status predicts
higher activity of a gene in the control condition,
it also tends to do so in the LPS stimulated condition.
However, we also observe this sort of cluster
of genes down here,
and what these represent are cases
where we have a dominance rank by condition,
by stimulation interaction,
and the direction of effect is where individuals
who are low status respond in an exaggerated way
to this LPS stimulation.
And that's another way of seeing that is shown here,
where the absolute value of the effect of stimulation
is shifted rightwards for low status individuals
in blue versus high status individuals in purple.
If we will look at what those genes are,
it turns out that they're highly enriched for genes
that are involved
in inflammation and innate immunity,
particularly the inflammatory response,
whereas genes that respond more strongly
in high status individuals
are also enriched for innate immune signaling,
but more so in association with the type
one interferon signaling pathway,
which is canonically associated
with the response to viral stimuli.
So what this tells us when we actually look at the biology
of the pathways that are sort of first line responders
to Lipopolysaccharide in a stimulation
is that we actually see effects of social status
at a very, very granular sub cellular level,
where cells from low-ranking individuals
actually sort of shunt the response down
this left-hand pathway, this MYD88-dependent pathway.
And cells from high-ranking individuals
tend to use an alternative pathway,
this sort of right hand TRIF-dependent pathway
at a higher rate. What does this mean?
It actually means that master regulators,
these transcription factors that control downstream
gene activity and control
much of the downstream response to infection,
are actually differentially regulated
in low-ranking and high-ranking individuals.
So this is a change that influences
not only overall gene activity, but gene activity in these very,
very targeted pathway dependent ways.
I told you, too, that we're able to watch the same individuals
when they're low status in the first part of our study,
and then high status in the second part of their study,
or high status to low status, and so on.
This is a measure, or measure of social status,
again, on the left-hand side,
and you can see that kind of change here.
So there is absolutely no correlation in our models
between the social status of females
in the beginning of the study,
and social status in the assumed females at the end of the study.
This means that we can also ask
about the long-term effects of social history,
about how an animal's past social experience
might continue to affect her down the road.
Okay. So here, I'm going to show you a little bit of data
that was collected one year
after we did that sort of mid-study rank rearrangement.
What we find again is that the rank of those individuals,
the social status of those individuals at the time
we actually sample in that second phase of the study,
influences the activity of lots of genes.
That's basically what I've already told you.
What we were somewhat surprised to find
is that the previous rank, the rank of those individuals,
the social status of those individuals over a year earlier,
which again is completely uncorrelated
with social statuses at the time of sampling,
also is significantly associated
with the activity of thousands of genes
in these circulating immune cells.
Even more remarkably, about 1,000 genes show evidence
of an interaction effect between past and current social status,
a social history effect that is strongly polarized
towards a greater memory,
if those females were socially disadvantaged in the past.
So I'm just showing you one gene here as an example,
this is current social status. And I've stratified the sample
by whether the sample comes from a female
who used to be low status,
who used to be medium status, or used to be high status.
Females who used to be socially advantaged high status
in their sort of former lives
are very sensitive to current social conditions,
where those who were socially disadvantaged
in the first phase of our study
are basically insensitive to their current conditions,
even if they're relatively high status at the time of sampling.
And this is a graph
that shows you a very similar kind of pattern,
but just aggregated across all 1,000 genes, right?
Females who were high past rank
are very sensitive to current rank,
females who were low past rank aren't.
And so what this is telling you, in other words,
is that it's those females who again,
were socially disadvantaged in the past,
who seemed to carry this baggage of social history forward
into the future.
So what we find overall through a series of studies
in these rhesus macaques is that differences
in the social environment, and what we interpret again
is the chronic stress of social subordination,
has what we interpret as a causal effect
on thousands of genes in circulating immune cells,
including the response to other types of challenge,
like an immune stimulation.
We believe these changes are linked
in part to transcription factor binding of DNA
to these master regulators of gene regulation,
that in turn change the actual polarization
of how different signaling pathways,
even in response to the same stimulus,
are used within the cell,
and that at least part of these effects
can be embedded into biological memory.
We don't know for how long but we can infer from our results
that this lasts at least a year, even in immune cells,
most of which are much younger than a year,
and even in adults who are not necessarily
going through these sort of critical periods of experience.
I just want to mention very briefly one
piece of unpublished data,
which extends some of this work to responses to vaccination,
and the adaptive immune system,
which we did by administering pediatric flu vaccine
to our animals
and looking at vaccine-specific antibody responses.
Here's a picture of an antibody response in our female macaques,
and then again, in a phase two of this sort of study,
and we find remarkably here is that measures of social status,
and even more strongly, behavioral measures
of the frequency of received harassment,
predict how strongly these animals
are able to respond to those vaccines
and mount an antibody response, which is something
that has perhaps particular resonance for us,
given our current environment as humans.
So in sum, I just want to say
that I think the emerging evidence,
especially across species evidence,
suggests that our dependence, the human dependence
on social relationships, and here again,
I'm talking about things like social isolation,
and social connectedness or relative social status
advantages or disadvantages in these sorts of small societies,
have much deeper roots in our evolutionary history.
These aren't things that humans have invented,
but rather we see some parallels in other species.
And that means that basic science in animal models,
just as it's been used to understand
the etiology of cancer or Alzheimer's disease,
or cardiovascular disease, may also help us untangle
some of the causal biological pathways
and mediating mechanisms that contribute
to the social determinants of health in humans.
And with that, I just want to thank the people
involved in this work,
particularly the trainees who led this work,
Joaquín Sanz, Paul Maurizio, and Joao Batista,
Noah Snyder Mackler and Ryan Campbell.
And just let you know that if you're interested
in this kind of work as it pertains to animal models,
I co-run a high-priority research network that is funded
by the National Institutes of Health
with Kathie Millen Harris at UNC and Alessandro Bartolomucci
at University of Minnesota, and we're very interested
in funding pilot projects and fellowships
for comparative studies, and so on.
So with that, I'll stop the share
and turn it back over to the whole group.
Dr. Sharon Jackson: All right, thank you very much,
Dr. Tung. Wonderful presentation.
Very exciting and we really appreciate
that last plug at the end for collaboration.
I'm sure that our participants will be very excited
about that opportunity as well. So I'd like to remind people
that you can post questions in Slido.
And just to get us started off, Dr. Tung,
I have a question and follow-up to your presentation just now.
And we will do questions for both participants.
One of the things that I found fascinating was,
what is the ability to change your social status
in these models?
I mean, it's clear that you've got a high social status
and a lower social status,
but is- it's a two-part question.
Is there an attempt to change social status?
And is that a possibility in these models?
Dr. Jenny Tung: Yeah, so I mean, here,
we did it experimentally.
And so experimentally, it's certainly very possible
to do targeted manipulations of the social hierarchy.
And that's been done not only in the species
I talked about, the rhesus macaques,
but it's also been done in mouse models of social hierarchies
and social defeat, as well. And I think that, you know,
offers a lot of opportunities to understand
the effects of social mobility itself,
and sort of resilience to social disadvantage
as it's represented in these models.
Sort of in nature, it depends a lot on the species,
and sometimes there are strong sex differences
within the species as well.
So for female rhesus macaques, and for female bathrooms,
which were the case I talked about here,
social hierarchies are nepotistic.
Females non-genetically inherit their status from their mothers
in a way that makes it quite stable over time.
Sometimes you see females who just, you know,
for whatever reason decided
that is not what they want to do.
And you'll see more social mobility than that.
But that's relatively rare, and because of its rareness,
it makes it sometimes hard
to get this sort of variation in sample sizes,
that we would like to be able to analyze that in greater depth.
Dr. Sharon Jackson: Great. Thank you very much.
I do have another question for you.
Would you comment on how animal models
may be used to study intergenerational health effects
of social determinants,
and which take much longer and time span
to observe in humans?
Dr. Jenny Tung: Sure, I'd love to comment on that.
Because the baboon study that I work on
is a long-term study that is multi-generational,
and I didn't focus on it today,
but we actually have started doing some work
on intergenerational effects, for example,
and it has some power, right?
Because one of the things- it sort of expands
more early-life affect work where we see- you sort of,
if you have multi-dimensional social disadvantage,
or multi-dimensional disadvantage,
in general, it has very, very powerful predictive effects
on health and lifespan. And what's very nice about this
is that those different dimensions of disadvantage
tend to be relatively uncorrelated
from each other in the baboons, allowing us to sort of identify
the things that may or may not have the largest effects.
And then intergenerationally, what we see
is that mothers who experienced
a lot of social disadvantage early in their lives,
particularly loss of their mothers when they were young,
also exhibit reduced ability to raise their own kids,
this sort of very generally
has some parallels to Dr. Collins's work,
their kids, you know, and this can be decades later,
are less likely to survive, to reach adulthood,
and that's controlling for their kids' immediate exposure
to social disadvantage early in life.
And so of course, we are very interested
in the mechanisms through which those relationships might occur.
We also see some long-term effects
of early-life on differences in DNA methylation,
and our animals measured in adulthood,
which again, is something
that I think Dr. Collins alluded to in his talk.
Dr. Sharon Jackson: Fascinating, and that's a perfect segue
to go back to Dr. Collins to ask some questions.
So I will pose a question for Dr. Collins now,
and then possibly, depending on the time,
we may have time for one more question,
but certainly in the breakout session,
we will have far more discussion on this.
So Dr. Collins, fantastic talk as well.
And do you have examples of how interventions
on social determinants of health affect birth outcomes?
Dr. James Collins: So interventions,
that's a good question.
I myself haven't looked at interventions
trying to address the social determinants.
What we have is just we've taken advantage
of natural experiments that have occurred.
I talked briefly about the upward mobility,
something similar in a different context
would be looking at affluent white women
who have downward mobility due to something happening
and their birth outcomes deteriorate
despite them being poor and affluent,
if they have downward mobility, things don't do well.
But in terms of interventions on the social determinants,
there was a study that looked at the stress
of having to work in a job
where you have little control over your workplace,
and this is looking in Europe,
and found that women who worked in high stress jobs
in terms of having no control
had more increased risk of preterm birth.
And the intervention was they actually look at women
who started to have some control over their workplace,
their birth outcomes improved.
But that was a European study. My brain is thinking,
but I'm not coming up with something
that explicitly answers your question.
Dr. Sharon Jackson: I think that's a foundational answer,
because, you know, I think that the shift to intervention
and, you know, dissemination of those interventional strategies
is always lagging.
Dr. James Collins: Yes, correct.
Dr. Sharon Jackson: And as you demonstrated,
there are studies that are taking this issue on
and combine them with what we've just heard from Dr. Tung,
and the ability to study these pathways
faster in the primate models,
will allow for some of these questions
and possible interventions to be integrated into,
you know, the disseminated reports on this phenomenon.
Dr. James Collins: Yeah, agree. Yep.
Dr. Sharon Jackson: Okay, and I do have one last question.
No, we may have time for another question.
I do have another question for Dr. Collins.
What's your response to Dr. Tung's work?
Do you see parallels and similarities?
And I guess we should throw this out for both of you.
Dr. James Collins: Sure.
Dr. Sharon Jackson: We'll start with you, Dr. Collins.
Dr. James Collins: Yeah, we both see parallels.
And I- you know, I wish we had communicated sooner,
though we try to. Obviously, there are parallels
in terms of the whole what happens early in life
can affect what happens later on.
And it's socially driven, and that the impact of,
in my work, looking at impoverishment early,
in her work, looking at social deprivation early,
those impacts are pretty, pretty, pretty tenuous.
Definitely see parallels. And we're in the process
of trying to address generational
across generations of humans, which takes a long time,
but we've been somewhat successful
going backwards to look forward.
So we have a dataset of women who were born in 1950s and 60s,
and their kids were born in the late 80s early 90s.
And now those children are about 30 years of age,
we want to look at their birth records,
and hopefully over the next few years,
we can actually start looking at some of this
across three generations, not just two generations.
But the whole concept of using a primate model
that has a little bit shorter lifespan,
to look at generations, is just very, very, very,
very intellectually stimulating. Yeah.
Dr. Jenny Tung: Yeah, you know, I see a lot of parallels.
I also see a lot of inspiration, things that work,
especially with sort of health in older age and lifespan
as outcomes, including linking to early life
in non-human primate animal models,
is so in its infancy relative to the long history of work
on the social determinants of health in human populations.
And so when we think about any kinds
of theoretical frameworks for our work,
or we're looking for, again, inspiration for models to test,
by talk to colleagues of mine who study human populations,
and especially, I have to say social scientists,
who, again, have been thinking about this longer and deeper
than those of us who work on non-human animal models.
So it was actually-
yeah, it was delightful to see Dr. Collins's presentation.
Dr. Sharon Jackson: Great, thank you.
And I think we have time for one last question for this session,
and then we'll move on to the second case studies.
And this is for Dr. Tung.
Does your work suggest social hierarchy is inevitable,
and should the goal be to decrease distance
from those at the top to the bottom
in the models that you're examining?
Dr. Jenny Tung: And so I think that if one takes
an evolutionary and comparative perspective,
and looked at sort of social hierarchy and social status,
you would actually very rapidly come to conclusion
that it's not inevitable. And that's because the models
that I talked to you about today,
the species that are in sex combinations
that I talked to you about today
tend to be pretty hierarchical species.
And that's where they make a good model
for what I was trying to study.
But there are actually a number of other
primates where hierarchies are their group living primates
are basically undetectable, or very, very flat.
And even within the species that I study,
which, again, they're pretty hierarchical species
that actually a term of art in primatology
for the rhesus macaques I had told you about
is that they are despotic species,
is that if you look social group by social group,
they're not all the same, right? We can draw like sort of lines
that describe how steep these hierarchies are,
and some are flatter than others.
It's just much harder to study that
because then you go from the space of N
equals the number of individuals to the space of N
equals the number of groups.
I think it's a fascinating question,
and understanding what mediates those differences
is definitely an area of interest for the future.
Dr. Sharon Jackson: Absolutely, thank you so much,
Dr. Collins and Dr. Tung, for your seminars here.
And everyone, we will have a breakout session
in just about an hour.
And we invite everyone to participate
in either this session or the breakout session
for our next set of speakers.
We're going to make a transition now
to the next breakout session.
Dr. Miya Whitaker: Good afternoon.
I am Dr. Miya Whitaker.
I'm a program director within the NIH
Office of Research on Women's Health,
and it is my distinct pleasure
to welcome you to today's case study session.
This case study session is focused on population science
and intervention science.
Before we get started, there are a few housekeeping items.
I just want to remind you
that there is live captioning available if needed.
If you have a question or questions
for either of our presenters,
please submit them using the chat function.
Feel free to indicate to which presenter
you'd like your question directed,
and I'd be happy to moderate those questions for you shortly.
We will now transition into our panelists' presentations
after which I will moderate a Q&A session.
And so we'll begin our case study session,
session two with Dr. Rachel Gold.
I want to welcome Doctors Rachel Gold
and Doctors Daphne Martschenko.
Dr. Gold is an epidemiologist and health services researcher
at the Kaiser Permanente Center for Health Research, in OCHIN.
Dr. Martschenko is a postdoctoral research scientist
at the Stanford Center for Biomedical Ethics.
And she is a co-organizer of the International Race Empire
and Education Research Collective.
I turn the virtual podium over to Dr. Gold.
The podium is yours, Dr. Gold, and thank you.
Dr. Rachel Gold: My pleasure. Thank you for having me today.
Can you hear me? Is everything good?
If I could get a verbal response.
Male Speaker: Yes, we do.
Dr. Rachel Gold: Okay, thank you very much.
I got my slides up and I can't see you.
Okay, terrific. Well, thanks so much for having me.
I want to just say to Doctors Tung and Collins,
that was an amazing set of presentations.
Dr. Tung, your work seems really fun.
I wish I had pictures of primates
to show you guys but I do not.
What I really like about the order
of how things have gone today
is we've talked about the structural drivers
of health inequities and social determinants of social risks,
and we've talked about some of the biological processes
that are involved. And I'm going to talk now about,
well, what can we do from a primary care perspective?
Like, how can we intervene?
And I'm going to talk about some work
that I've been doing in that space.
I think it'd be really nice transition.
So just to get started, I know this group all knows this,
but my team just recently published a paper
in the Journal of Preventive Medicine
showing that among community health center patients,
that's CHCs, with diabetes, who had a number of different
social risks, housing or transportation needs,
essentially no less likely than those
who didn't report having those risks
to be up-to-date on their diabetes kick, right?
They're getting the same quality of care,
regardless of whether they had a social risk,
up-to-date on A1C, screenings, et cetera.
But even among those who were up-to-date on care,
those with food insecurity had lower rates of controlled A1C,
those with transportation insecurity
had lower rates of controlled A1C, blood pressure and LDL.
And surprisingly to me, those with housing insecurity
did not show differences in those measures,
but clearly, they were associated
with food and transportation. And our take-home here,
which again, won't be a surprise to anyone in this audience,
is that just because someone guideline can coordinate care
isn't enough, because social risks
affect health outcomes way beyond access to care.
I'm going to try to go to the next slide.
Correct. Terrific.
So the National Academies in 2019 put out a report
on integrating social care and delivery of healthcare,
and they came up with what they call the five
As of social care integration, which is again,
how do we just use social risk information
in a clinical setting?
And I'm going to talk to you about a few of those today,
but we're focusing on adjustment.
The activities that are focused on healthcare delivery
fall into three categories.
I'll start with awareness, which is simply do we know,
does the care team know that there are factors
affecting a patient's ability perhaps to act on the care plan,
that they have social risks that are affecting their health?
Do they- assistance is really what we mean,
we talk about basically making referrals
to social service agencies, foodbank,
housing support, transportation,
or providing those services directly from the clinic.
Adjustment, which is where our team is focusing is,
can you adjust the care plan
so the patient who has perhaps some kind of social risk
is more able to act on the care plan?
And I'm going to talk to you about this quite a bit.
So what might adjustment activities involve?
For example, they might involve making sure medications
are affordable, making sure the patient
is prescribed a generic prescription
or a polypill where there's less of a copay.
Can you mail the prescription to their home
if they've got transportation insecurity?
Can you make follow-up care?
Can you space out the visits more
so there's not as much of a transportation barrier?
Can you provide telehealth? If someone is houseless,
can you avoid refrigerated medications?
If someone is food insecure and has diabetes,
do you need to think about modifying the insulin dose
for when during the month their food benefits run out?
These are just a few examples of many.
There is some beginning evidence
that shows you do get better clinical outcomes
when these adjustments are made.
But there's also evidence primarily
from my colleague Saul Wiener,
that adjustments only occur less than a fourth of the time
in many different care settings,
even when there's information presented about them.
So what is the best way to present social risk information
in the clinical setting-
and again, I'm mostly focused on primary care-
to help increase the provision of adjustments?
Might tools in the electronic health record help,
reminders, summaries, recommendations?
So we've got a study right now, I'm co-PI
with Dr. Laura Gottlieb at UCSF. It's funded by NIMHD.
Thank you, we really appreciate it.
And we believe it is the first study
to develop clinical decision support
targeting the provision of social risk informed care,
which is another way we talk about adjustments.
And the study is called CO-HERE.
Here's where we are, we spent about a couple of years
developing a set of tools,
working with CHC staff, diverse staff.
We looked at how those tools work and some pilot clinics,
we are now revising the tools
based on what we learned from the pilot process,
and we are about to start testing of the tools' impact on-
and we're looking at hypertension and diabetes.
We're right in the middle.
We are in the revision process right now.
And I'm going to talk to you a lot
about the content of the tools,
I think you'll think it's interesting.
Just in general, the COHERE tools,
this is the pilot version that we initially tested, again,
it's targeting care plan adjustment
in hypertension and diabetes,
and also helping to document those adjustments.
So the in brief, though, this is the way the tools work,
if the patient as we've been calling a clinical red flag,
uncontrolled hypertension, or diabetes,
or a high no-show rate for visits in the last year,
and if we know that they have social risk information,
or if that information isn't there,
and I'll show you that in a minute,
that is what activates the tool.
So those are the patients for whom these tools
become available in the EHR.
I'm not going to make you look at all of this text,
I want to just draw your attention to the first category-
excuse me, the first column,
to just note that there are three broad categories
that the pilot version of the tools fell into.
One was around awareness, not actually adjustment,
awareness has to precede adjustment.
But can you increase- how can we use these tools
to increase social risk awareness,
increase social risk screening,
and increase documentation in the EHR?
The second category is can you facilitate
and document patient provider discussions about social risks?
And can we also recommend
and also document care plan changes?
And again, a lot of the examples are quite like the ones
that I talked to you about before,
you'll be able to see these slides,
I know, on the website.
And I'll get into the stuff in more detail.
So I don't want to linger too long on all this text.
But the pilot clinics, here's what they like thought
about those pilot tools, the pilot version of the tools,
which I thought these responses were totally fascinating.
The most profound thing we heard was,
these are called Community Health Center staff,
and they said, "We already do this.
We don't need your tools to suggest
that we make care planning adjustments.
We do that. That's our job.
Don't tell us how to do our job."
They didn't like that. They didn't like recommendations
but they did like the documentation aspects
that helps our team communicate better
about the adjustments we're making.
They love the reminder to do the screening.
They love the reminder to document patients'
social risks in a standardized way using Z-codes.
They wanted fewer clicks, they wanted less text.
They wanted a one-click tool
to document provided adjustments.
And one suggestion that came up that we ran with was,
could they work like a well-baby visit checklist?
We discussed the following.
So here's how we revised those tools.
We kept the reminder alerts,
"Hey, this patient is due for social risk screening,
hey, do you want to document this recently-
this patient's social risk
as in the problem list with a Z-code?"
We got rid of tools that were in Smart-Sets,
which were kind of like order sets,
these recommended actions and preset orders.
They didn't like it.
They said, "You're telling me how to do my job."
We did add a documentation checklist.
So I'm going to show that to you in a minute.
With a list of potential adjustments
and the way we framed it,
as you'll see, it basically says,
the patient and I talked about the following.
So without actually suggesting folks do stuff,
we do have a list there that kind of serves two purposes.
Right? One is to be able to let folks do the documentation:
Yeah, I took care of this. We talked about the following.
And then you put it and add it to the note or the problem list,
but it also by being there inherently might,
you know, trigger some:
Oh, yeah, I should talk to that patient about that.
It might it, you know, just in case they forgot.
And in general, the tools look at managing medication costs,
this is driven by our stakeholders' interest,
titrating insulin based on food availability,
changing follow-up encounter modality and timing just again
to make sure you can get to that follow-up care,
and adding relevant information about all of that
to the after-visit summary
so the patient has a record of it as well.
One thing that is new in the tools
that we're going to take to trial
is that there are some choices that study clinics
are going to have to make.
One is, do you want to put this checklist-
which again, I'm going to show you soon-
in an existing note template?
So if you've got a visit note template,
it'd be pretty easy to just stick this checklist into that,
but not all clinics use that.
Have a rooming staff start a note
for the provider using a smart phrase,
or which is a text shortcut. Or have- or by having the tools
just right there in the problem list
or in the- oh, excuse me,
allowing the support staff to start the note
or allowing the support staff to do this documentation
because every CHC has different approaches
to this kind of which staff roles
are allowed to do which kind of documentation.
So in some, the tools that we are going to take to trial
are designed to,
one, identify the social risk screening is due,
and it's basically every year,
which sort of needs to be refreshed,
with a one-click link to conduct
that screening documentation in the EHR.
Another is an alert that says, "Hey, do you want to add
this Z-code to the problem list or the visit diagnosis?
Documenting the discussions about the social needs
and the related adjustments so everyone's aware of them.
Documenting socio-economic reasons,
costs specifically for medication non-adherence,
and also reviewing external formulary information.
Again, these are all steps to try
and ensure that medications are affordable.
And we're focused on medications,
and again in diabetes and hypertension
based on our CHC staff who helped design these tools.
We were going under their direction.
So I'm going to talk to you
a little bit about the specific tools.
One is- and BPA stands for Best Practice Advisory,
it's an alert in the EHR.
This one appears to usually the rooming staff,
but it's really at any encounter
where social risk screening is done.
So that might be a community health worker,
might be a social worker.
But in an encounter where that kind of screening is done,
that's where that shows up.
It pops up if the last social screening
is more than a year old.
And we focused on financial housing,
food, transportation and utilities and security.
It says social determinants screening is out of date,
it shows the results from those recent screening
and the last date screened
and gives you a quick link to complete the screening.
Pretty straightforward.
And then there's the Add Z-code to problem list alert.
And so the patient has a newly documented social risk status,
and that could mean they newly reported a social risk
or they reported it was resolved:
so I used to have housing security,
but now it's resolved. That shows up either way.
It's on the day of the screening.
It's generally for rooming staff,
when screening is entered.
If there's a medical red flag present, as I described before,
that appears for the provider until either the red flag
or the social need is resolved.
And we are going to see how people like that
because we know people don't like seeing alerts continually,
but we determined that that was important.
If the screening is not addressed,
if the patient doesn't get screened,
both the provider and the rooming staff,
again, will get a reminder about that as well.
The alert says "add this to the visit diagnosis."
If it's a newly resolved risk, as I said, it says,
"Do you want to resolve this on the problem list?"
And then it suggests going to the checklist to document the-
or using the Smart Phrase, Smart Text,
to document the adjustments that you have made
or and discussed with the patient.
And again, there's a link to the assessment and plan note,
which is part of the problem list.
You can also enter Z-code of the visit diagnosis
or to the problem list if it's not already there.
I'm going to show you- Oh, it's coming right up.
So once again, where is this checklist
that I which I speak so much?
Again, it can be accessed through note template,
it can be accessed in the problem list.
Once the Z-code is entered, it just shows up, here's-
and it's customized given to a specific patient's data.
It can also be added manually.
And here's what it says at the beginning,
patient has recent whichever red flags triggered this,
HA1C is too high, blood pressure is not controlled,
and or history of no shows,
that will be customized to the patient,
and socioeconomic barriers as of the following date.
Based on the social risks documented in the chart,
we discussed dot-dot-dot. And here's what it looks like.
I know the language there isn't exactly what I just said,
you were still dialing in the actual-
the exact language it's going to say here,
but you can see what this looks like here, right?
Here, first, it's- you can add social risk education
to the after-visit summary.
There's a quick text link for that.
But here's what I really want to show you is this dark blue,
based on the social risks documented in the chart
to facilitate care plan adherence, we discussed,
and this is just a part of what's on the list.
And again, what's on the list would be also customized
to a given patient's circumstances,
but they could select talking to the pharmacist
about help lowering medication costs,
home delivery, 30 versus 90-day prescriptions,
again, for a less of a copay, a longer and it will follow up,
following up via telemedicine et cetera.
And there's also an- and I'll show you the details of that,
but I want you to see what it looked like in the EHR.
So here on the leftmost column is I think the complete list
of what you might see in that Smart-list text,
we discussed titrating insulin,
possible generic or formulary substitutions.
And this is what would show in the user, the clinical side,
and added to the note. And then on the patient text,
if you want to add it to the after-visit summary,
you would say you and your provider talked about
how to adjust your insulin dose based on your food intake.
Okay, so possible generic or formulary substitutions,
using GoodRX discounts
is a way to find a lower cost prescription.
Hey, the patient- the discount code from some
GoodRX may help, you can use this.
We discussed patient preference around prescriptions
in a role follow up, following for telemedicine,
the three- the ellipse here
means that there's an ability to add free text.
So other barriers to the patient's
taking medications as prescribed,
and then they can fill that in. And we discussed the patient
talking to the pharmacist about opportunities
to lower medication costs. And in the AVS, it says,
"Ask your pharmacist about ways to lower your costs."
Yeah, so let me move on.
Okay, and a couple other tools in this suite of tools we built,
I'm finished talking about the checklist.
There's a BPA that's within medication adherence,
and medication adherence is a very standard part
of rooming in primary care, I'm sure you know that.
But if a patient's got a medical red flag,
and any outpatient prescription, it shows up
when in the rooming best practice section of the EHR.
And if the patient's medication adherence
has not been documented, then they see this alert saying,
"Hey, medication documentation-
med compliance documentation is not complete."
And then there are reasons that are given;
taking, not taking, taking differently.
And there's an option which includes cost.
What we did that it's-
most of this was already in the EHR,
but what we added was that now
when they go into medications and orders,
it highlights the reason the patient is not taking it,
and the date it's reported, and that was to really try
and make that front and center.
And then when other medications are being ordered,
not in the medication compliance,
but just during med orders,
when there's a related order entered in the EHR,
and again, the patient's got a red flag in any of these risks,
there might be alerts related to titrating insulin again,
this medication is not available as a generic
or you're not prescribing a generic,
prompt about barriers to taking medications,
and consider patient preferences around dispense again.
And then there's also ability to add it with one click
and add a note to the pharmacy to all of these points.
Okay. Look at that. Great.
And we are about to recruit clinics for our main trial,
and that's what we're doing.
So I'm not aware of anyone
who's tried to do anything quite like this before.
I'd love to hear about it if folks are aware of that.
And I managed to get my talk in 15 minutes.
I'm going to stop sharing my slide
and turn it over to my colleagues,
and look forward to hearing your questions.
Dr. Miya Whitaker: Thank you, Dr. Gold.
This is an amazing presentation.
We're so grateful to have you here
to talk about this wonderful tool
and it definitely represents a great first step
in our enhanced ability to meet people's social needs.
So this is wonderful.
Dr. Rachel Gold: We'll find out if it does.
Dr. Miya Whitaker: Yeah, agreed.
Dr. Rachel Gold: I hope so.
Dr. Miya Whitaker: I hope so too.
And it definitely seems like it has a lot of potential.
So thank you again for sharing with us today.
So I now turn the virtual podium over to Dr. Martschenko.
Dr. Martschenko?
Dr. Daphne Martschenko: Thank you so much.
Thank you, Miya.
Thank you everyone who's played a role
in putting together what a wonderful workshop
it's been so far today.
I just want to check and make sure that Annamaria is here
and that you're able to unmute when it's your time,
and I will screenshare.
Okay, great. Awesome. Good to see you. All right.
So, thanks so much, again for having me.
My name is Daphne Martschenko.
I'm at the Stanford Center for Biomedical Ethics.
Hopefully, folks are seeing the right screen,
but please unmute if you are not.
What I want to talk through today is first,
introducing community engagement.
Some of the presentations we've heard today
have touched on elements of this,
but I'm going to do a deep dive into it a little bit.
And then I'm going to discuss the problem
of shallow engagement
and the need for community empowerment.
And we'll go through a case example,
a co-design process
that works to achieve community empowerment.
My presentation today is going to draw on two manuscripts,
one of which is forthcoming
and the other one which is under review.
And I will make sure that we can share the link
to the preprint of that bottom
one sometime during today's presentation.
But first, what is community engagement?
So community engagement is about garnering public recognition
as a social value of research and clinical services.
It is about respecting participant
and community values,
building trust, and also empowering those
who are traditionally left out of decision making processes.
That's a high level overview.
But there are also individual level and system
level objectives to community engaged research,
community engagement.
So some of the individual level objectives here are:
one, to build rapport between researchers,
clinicians, participants and communities.
Another individual level objective
is to enhance researchers' or clinicians' cultural humility,
and then also to increase researchers'
and clinicians' awareness of unconscious bias
of social political contexts, power structures,
and social determinants of health.
System level's objectives for community engagement
include understanding stakeholder's real life
needs and constraints,
empowering underserved communities
in health research agenda settings and processes,
and increasing the trustworthiness of research
in clinical institutions.
This approach to doing research and clinical translation
prioritizes co-learning with power sharing,
it also prioritizes local rather than global knowledge.
And as I've said already, community empowerment,
and social and responsive justice.
And one of the critical elements to community engagement
is power, benefit, and responsibility sharing,
which I'll get more into in a moment.
Other points to consider, so the data that is generated,
that is collected through community engaged work is local,
and it requires participants' knowledge of all uses.
It is also acquired via long- standing trusting relationships
between researchers, clinicians, and community members,
and informed consent,
which often gets mentioned is a living document.
So it's a formal form,
but it exists alongside informal agreements
that develop over time.
So I think one of the key take-home messages here
is that the simple informed consent documentation
that we have our study participants sign
is not the end all be all,
when it comes to community engaged research,
there is a series of informal contractual
re-consenting processes in the dialog
that is formed between researchers
and between the community members.
So as a kind of summary here,
community engagement really prioritizes power, benefit,
and responsibility sharing,
it is not prioritizing data sharing to the same extent
that we might see in, for example, secondary research.
And so I want to emphasize here
that it's really more than just listening to context experts
as was mentioned,
you know, earlier today in the introduction,
there was a discussion about the importance
of hearing the perspectives of context experts.
And in addition to listening and hearing those perspectives,
it's also about in community engagement work,
responding and taking action,
such that decision-making power is shared.
So that's what I mean by power sharing,
decision-making power is shared.
The benefits of doing the research
or implementing the clinical program are also shared,
and researchers and clinicians
are compelled to share the responsibility
to act in the interests of community members.
And so including context experts,
it's critical to ethically and socially
responsible research and research translation.
And if we're to take seriously the role
of social determinants in health,
then we need to enhance these social aspects
of research and clinical care.
And by that, I mean that us as researchers,
as clinicians, we need to listen, respond to,
and integrate the perspectives, values,
and needs of those that we're intending to help.
And we need to do so early, and we need to do so often.
The problem however, is that we don't always do this,
and that's what is called sometimes shallow engagement.
In shallow engagement, researchers, clinicians,
make assumptions upfront about communities
and then use engagement tools
as a way to validate those assumptions.
In shallow engagement, communities
are also brought in after research
has already been conceptualized or even conducted.
And communities are asked to comment
on already prepared materials rather than to be involved
in the very creation of those materials.
So this is one thing that I really appreciated
about Dr. Gold's presentation
is working with the actual stakeholders
to try and understand what is their reactions to the materials
that are being produced before they're fully rolled out.
And so lack of power sharing between communities and research
is one of the outputs,
one of the byproducts of shallow engagement.
So another way to frame this would be that
in shallow engagement,
the focus can be more so on data collection and data
sharing than on power, benefit, and responsibility sharing.
So thinking of community engagement as a means to an end
as a way to recruit people for inclusion in our research,
while that is important,
there is another model with community empowerment
that offers perhaps a bit deeper form of engagement.
So the need here is for community empowerment.
This is a shift from shallow to deep engagement.
Emphasis is placed on creating respectful relationships
with communities, and community members
are enabled to name the problems and solutions for themselves.
So it really is emphasizing that power, benefit,
and responsibility sharing that I mentioned earlier.
Priorities for community empowerment
are to build reciprocal relationships
and true partnership.
So this requires true power sharing built
on mutual respect and benefit. It also prioritizes co-learning,
so it acknowledges the importance
of everyone's contributions, individual expertise,
it's working to try and disrupt
the dichotomy between the expert and the public.
And finally, it also prioritizes transparent
and shared decision making.
So it heightens the accountability of researchers.
And a key message that is important in community
empowerment is being able to document
and demonstrating the impact of community participation,
community perspectives, values and needs in decisions
that are then made about research
or about clinical program.
So why community empowerment?
Community members, as has already been mentioned today,
are experts in community and healthcare experiences
in public interest. They are context experts.
And local communities, patients and their families
are the ones most likely to shoulder the harms
from the inadequate implementation
of new technologies or clinical services.
So in situations where uncertainty is high,
shared decision-making with community collaborators
is a means for arbitrating on hypothetical benefits
and risks while also ensuring that there is alignment
between healthcare systems,
and community values, interests and resources.
And so as I've mentioned, you know, these parties,
these communities, these patients, their families,
they're the ones most likely to shoulder the harms,
the burdens of uncertainties, and so their voices
should be a driving force of clinical translation.
And we see uncertainty as an opportunity
to evaluate community interest and preferences
proactively rather than reactively.
So right from the very start.
And so that brings me to the core of today,
which is an example of a community empowerment program
process that I've been a part of,
and we have a number of our community members
and my co-facilitator joining us here today,
and I'll be handing it over to one of our community members,
Annamaria, to speak in just a moment.
But to provide some background, some context for this project,
part of the motivation is the diversity problem
that is in genomics.
I'm sure many people here already are aware
that there are Eurocentric biases
in genome-wide association studies, one of the emerging
and increasingly used methods of genomic analyses.
And these biases in who is included which dataset,
which individuals are recruited for research fails
to make the benefits of genomics accessible,
and it also risks exacerbating health disparities
if and when these genomic technologies
are actually translated into clinical settings.
And so there is a high level of uncertainty
around the utility and validity of polygenic scores,
which are a aggregate composition
of the genetic effects
that are identified through genome-wide association studies.
And given the high level of uncertainty and utility,
high level of uncertainty around the utility
and validity of polygenic scores,
there is a fear that focusing on polygenic scores
will risk overly
emphasizing genetics and ignore social determinants of health,
and also risk placing responsibility on the individual
in a way that allows society
to absolve itself of responsibility.
Finally, as I've mentioned,
and I will continue to emphasize,
local communities, patients and their families
are most likely to shoulder the harms
from inadequate implementation of new technologies,
in this case, polygenic scores being one of them.
And so this is kind of the background context
that led to our community co-design,
the problem of diversity and genomics,
the high level of uncertainty around the utility
and validity of polygenic scores,
but the practical realities of the fact
that we are already seeing instances
in which individual researcher's clinical programs
are trying to incorporate polygenic scores
into the provision of clinical services.
So this community co-design,
I'm just highlighting for individuals in particular here,
although there's a much larger team of us behind this effort,
Hannah Wand was my co-facilitator on this project.
And then we had a number of community members,
but I particularly want to highlight Ting Pun,
Sheryl Michelson, and Annamaria Smitherman,
who worked with Hannah and I to write a manuscript
documenting what this process was like for all of us.
So in the community co-design, some of our goals here
were to support the precision public health services
and improve the prevention of chronic disease,
and to design a clinical program
in a way that is responsive to an inclusive
and respectful of the local community.
So in this case, this is the Stanford healthcare system,
the patients, the caregivers within Stanford healthcare.
And also, we wanted to decide alongside these community
members whether and how to translate polygenic scores
to clinical care.
So critically, we weren't coming in with the decision already
that there was going to be a preventative genomics program
in which polygenic scores were going to be utilized.
We wanted to first take it to the people
who would be the "end users," or the ones most impacted
and the ones that researchers are keeping in mind
when they're doing their work, the communities that would be
utilizing this technology in a practical setting.
So we had eight community members and two facilitators,
myself and Hannah, we met bi-weekly
from May through August of 2021 on Zoom.
Each meeting was approximately 90 minutes
except for our first session, which was two hours.
And at Stanford, we have a Patient
and Family Partner Program, or PFAC as it's abbreviated,
which is comprised of individuals
who are both patients within Stanford healthcare,
as well as caregivers to patients
within Stanford healthcare, who volunteer their time
to being part of initiatives such as this.
Our first meeting focused on introductions
and setting group expectations and norms,
and we then had three didactic sessions.
The first is on preventative health and risk stratification.
The second was on polygenic scores for risk prediction.
And the third was on data privacy of genetic information.
So these were meant to help everyone start
on a level playing field in terms of the knowledge about,
you know, polygenic scores, preventative health,
how it currently operates,
and how genetic information is protected
from a data privacy perspective.
We then had three open-ended sessions.
One was on informed consent for the utilization
of polygenic scores and clinical settings.
The second was on equity
and access to polygenic scores and genetic services.
And the third was polygenic results and long-term care.
And then we had a final session with all the community members
in which we collaboratively summarized
the community feedback,
including whether community members saw value
in implementing a polygenic score program,
and how community feedback
should be integrated into its design.
So as I mentioned earlier,
a key component of community empowerment
is not only allowing community members
to name the problems and solutions for themselves,
but also demonstrating how their participation in the research
or the design of the clinical program
is actually resulting in changes,
so really emphasizing the shared power
sharing in the decision-making process.
I now want to hand it over to Annamaria
to kind of talk through what the experience was like for her.
And so to do that, I'm going to stop sharing my screen
so that we can make sure to see her.
Annamaria Smitherman: Thank you, Daphne.
Yeah, I was really delighted
to be a part of this research group.
I learned, you know, so much, and as a teacher,
that's always exciting to me.
I think that the reason that this group really worked for me
came down to three parts.
Daphne and Hannah, and we used first names
as part of that equality and power sharing issue
that is subtle, but important.
Daphne and Hannah were very intentional
in everything that they did.
And it felt like not a moment was wasted.
There wasn't dithering, there was thinking
and there was reflection,
and there were moments of quiet pausing
while considerations were made,
but the work was intentional and intentionally inclusive
in the sense that everybody in the group was clearly heard
and given opportunities to be heard,
even folks who were sometimes quiet,
space was made for them to be heard and connected.
And that was really important. So the intention was there.
The second part that was really important to me
as a member of the community
was that we revisited issues on a regular basis.
So we were given new information,
Daphne just talked about the three informational meetings,
it wasn't done then, you know, we could come back to that.
And we came back to issues over and over and over again,
and looked at them in new ways
or having thought about something.
We wanted to offer more input that was allowed
and encouraged and welcomed.
So the intentionality, the revisiting,
and then finally, the educational part
was really crucial
to teach us everything that we needed to know
in order to make confident and thoughtful recommendations
about this potential program for Stanford.
So, I don't know, Daphne,
is there anything else you want to hear or like to know?
Dr. Daphne Martschenko: That's great, Annamaria.
And I hope that, you know,
when we get to the question and answer portion,
people could feel free to also direct questions
directly to Annamaria, as well.
Annamaria Smitherman: That would be great.
Dr. Daphne Martschenko: Thank you.
Okay, so I know we don't have too much time left,
and I'm eager to get to the Q&A.
So I just have a couple more slides.
I wanted to talk through some of the findings
or results from this experience. And as Annamaria said, you know,
the education really has been ongoing.
And we continue to work with these community partners,
even though we're not having these kinds
of formal regular Zoom meetings anymore.
So there were some initial considerations
that the individuals involved in the design
and conceptualization
of preventative genomics program had coming in.
One was that there was knowledge from the literature,
as I've already mentioned,
that polygenic scores have a high level of uncertainty
and there's debate over their validity and utility.
But as we have seen, increasing examples of polygenic scores
are currently being utilized
or starting to be utilized in clinical settings.
And so some of the review of the literature
suggested to us that polygenic scores may be scaled out
and supported through population health services,
but specific provider training needs
and infrastructural supports for this are unclear.
PCPs seem to find risk conveyed by polygenic scores
intuitive with current risk prediction
and multifactorial disease models,
but it's also a numerical value
that is grounded in a population distribution
and can be incorporated into existing
clinical risk calculators used in preventative screening.
And so the underlying genomic methodologies and limitations
are less familiar to providers
and should be the focus of future training.
So this is what some of the literature was suggesting.
But then we took it to community partners
and wanted really to incorporate their perspectives
in deciding who should be trained
to provide this information if it is deemed useful.
We heard that patients are more likely to engage with new
and possibly sensitive information
if they trust the healthcare system,
which I'm sure is not going to be a surprise to anybody here.
But the current uncertainties around polygenic scores,
their limitations and clinical validity
require attention to informed choices about polygenic testing,
it can't simply be offered
across a healthcare system by anyone.
And so people felt that genetic information polygenic scores,
even though it's a numerical value,
it's still grounded in genetics, it feels sensitive,
it feels emotionally latent to our group,
and so provider capacity to support polygenic score
testing decisions should require skills
and assessing personal values and personal utility.
And providers with proper training
are not helpful if they're not available in a reasonable time.
And so access is an important dimension of quality care.
It's not only who do people trust,
but how available are those people and equipped
to provide the information in an appropriate way?
And so this translated
into a couple of programmatic decisions.
And one of the big lessons from this co-design process
was that service delivery is an opportunity for building trust
and decision should not solely be driven by logistics,
by what's going to be the most logistically simple approach.
Provide suggested questions and educational materials
for the patient pre-visit to prepare patients
to think about their values in advance
and to maximize provider-patient discussion during the visit.
And also make sure that provider training
is not just about education,
you know, education about what polygenic scores
are and what information they capture
and how they might be used, but also in skills development,
thinking about how to interface
with patients to understand their values,
their needs when communicating with them.
Patient advocates can help providers to understand
the importance of personal utility and test decisions,
as well as the emotional nature of genetic information.
And finally, care should be coordinated
through team-based care and provider handoffs,
if different specialists are involved,
this provides stability that is needed
for long-term preventative health discussions.
Finally, I just want to acknowledge
that there are some limitations to what we've done here.
One is that the co-design process
like other forms of community engagement
is not immune to the traditional approaches to compensation,
the limitations of traditional approaches to compensation.
Community members are not full-time researchers,
not full-time clinicians,
they are really volunteering their time
to be a part of this process. And I think as a system,
we still need to do a much better job
of offering the appropriate compensation
to allow as many individuals
who wish to participate in this process be able to do so.
And also there are challenges with community
engaged initiatives that more broadly occur
in terms of diverse and inclusive recruitment
and bringing on community members
who have historically been exploited
by scientific research.
So in summary, there are very real uncertainties
and concerns about balancing the benefits
and risks in population implementation.
And communities and patients as the intended beneficiaries
are the ones that are most likely to shoulder uncertainties
and so therefore should drive decision-making.
Community empowerment requires a commitment to partnership
and adapting to shifting group dynamics and rapport.
And it also requires institutional support
facilitator training,
community relationship building, power sharing and time.
Time is another key thing that, you know, in academia,
we often want to work as quickly as we can
to get the next paper out to secure the next grant.
These kinds of relationships really do take time.
Finally, investment of resources
and time to community empowered co-design.
We believe that it is aligned with the stated long-term goals
of most healthcare systems
to support person-centered and value based care.
And I'm really looking forward
to having a discussion here as a group
and to the breakout sessions following on from this
so that we have more time to talk through these things.
But thank you so much for having me
and for having Annamaria, and Hannah and Sheryl,
who I know are all here in the Zoom room today.
Dr. Miya Whitaker: Thank you so much
for your great presentation. We so appreciate it.
You and Dr. Gold have been absolutely amazing
So first, we want to start with a question to Dr. Gold.
As you know, social risk research
incorporates an equity conscious approaches,
and it's intentional about engaging community partners
to offer
an increased opportunity to address unmet needs,
given disparities and burden of health-related social risks,
really highlight an important area of focus for investment
and new programming and policies.
So with that in mind,
are there opportunities within the COHERE tool
to sort of link community members to services
which leverage community partnerships for OCHIN
and Kaiser Permanente?
Dr. Rachel Gold: So you're talking about
assistance strategies
linking patients to community services.
You know, I think that's probably
the next step in the research.
And there is work going on around
how to do assistance strategies.
I think something that we need to be clear about
is there's only a very beginning body of evidence
that these referrals actually make a difference
in terms of health impacts.
The emerging body of evidence says it does,
but it's still emergent.
I would point you to Dr. Gottlieb had
an evidence review a couple years ago
that basically says we don't really know
this makes a difference,
but intuitively, one thinks it would.
So I guess I would just say
that's not what this project is focused on.
We're focused on adjustment, care plan adjustments.
We did think that we had to- it took a while for us
when we were working with our CHC staff
and clinicians in developing the tools
to kind of get folks' heads around that.
We're not talking about making referrals in this project.
We're talking about adjusting care plans and documenting that.
But of course, that's part of it.
Right? That goes back to the five
As of integrating social care in the medical care, right?
One of the five
As is assistance, can we mitigate?
Can we get rid of the social risk
to begin with by getting folks connected to services?
But this is another one,
which is can we then adjust the care plan?
Because you can't always mitigate.
So can you then do something else
that would make a difference, that makes sure at least
that the patient get the med into the body,
get the follow-up care that they need
while we try and fix these larger social problems,
or at least get folks, you know, referred to services?
I mean, I would say I'm very, very cognizant
that the work we're doing is a downstream solution
to an upstream problem, but I am, you know, some-
we're trying to do it that way, and while hopefully others
are working on the upstream source as well.
Dr. Miya Whitaker: So in relation
to what you just mentioned, did you encounter any resistance
to tool implementation or tool use among the clinic staff?
And was there anything that you had to do
to sort of increase adoption?
Dr. Rachel Gold: Yeah, that's a great question.
I'm an implementation scientist,
as well as a disparities researcher,
and I can tell you that building tools
does not mean tools get used.
Sometimes the way that we do work on my team
is first we try and see
if there is an effectiveness when a tool is used
before you try and really focus on further implementation.
Because if it doesn't help,
there's not really a need to focus on, you know-
I mean, I'd like to make sure we got a tool that's really
effective before I work on trying to get people to use it.
But it becomes quite a bit chicken
and egg thing in the research that my team does,
because you can't not have people use the tool,
then it's hard to get the, you know-
it's hard to test that effectiveness.
So the short answer to your question is,
in the pilot clinics,
because we haven't gone to trial yet, in the pilot clinics,
we provided a little bit of implementation support,
we saw pretty varied uptake of the tools,
but people did respond to the tools around-
There was fairly high uptake of like the tools
to support documentation.
And then what we had, as I said, again, was a lot of pushback
on the way the tools around adjustment were structured.
So we have modified them, and we'll see what happens
in the next set in the trial clinics,
we're going to provide a little bit more implementation support
to some of the trial clinics,
but we weren't really set up to do that.
It's always a tension in this work, right?
Because you want to see
how do something work in the real world,
like just turn the tool on and see what happens.
Ideally, if it's a good enough tool, people will use it.
In reality, that is far more complex.
My guess is that what we'll do with this study
is demonstrate effectiveness when used,
I mean, that's the hypothesis. And if so, then we would-
our next study would be how to enhance adoption.
Dr. Miya Whitaker: Thank you. Great response.
So helpful. Daphne, we have a question for you.
So in terms of genetics and genomics research,
we know that it's like undergoing exponential growth,
and the community engagement really does
play a pivotal role in that because it sort of enables us
to access previously underreached communities,
particularly in terms of facilitating
that bi-directional bio log to build trust,
provide education about health risks,
and health in general and also empower communities
to pursue both screening and care
and also sort of enter into the engagement
with health-related research.
Given this type of research involves biospecimen collection,
which can have some specific cultural significance,
are there tools that your team uses to sort of help respond
to questions and concerns about exploitation,
benefit-sharing, responsiveness to research-
researchers' responsiveness to community needs, excuse me,
and now there are nuances for you that are different
when you think about genetics and genomic research
versus other types of research.
And Dr. Gold, if you have some thoughts on that,
feel free to share as well.
Dr. Daphne Martschenko: That's such a good question.
Thank you for asking it.
I think that was one of the key reasons that we spent
and devoted a lot of time to talking through
whether people felt that polygenic scores
felt materially different to our community members than,
you know, having their genetic data
communicated to them through like a genetic counselor
if they did carrier screening or something like that.
So we wanted to understand if the numeric value
that is a polygenic score,
even though it's a numeric value,
if that felt different to community members
than other sources of genetic information,
or other sorts of information
that are derived from genetic data.
And, you know, I can have Annamaria weigh in here,
but I think community members did feel like polygenic scores
are sensitive information, it feels very personal to them.
And it's one of the reasons that I have been working
with a couple of authors to comment
on a piece around data sharing,
secondary data sharing, and community engaged research
and the tensions that are generated
between the goals of data sharing
and the goals of community engaged research
is part and parcel,
because when we think of secondary data analysis,
there's a level of separation between the researcher
who's doing the secondary data analysis
and the individuals who first gave that data.
And in particular, if that data was gathered
through community engaged research,
where trust building is so crucial,
you know, trust can't be sold,
it can't be bought, it can't be transferred.
And so there is a lack of responsibility
sharing that is expected of researchers
doing secondary data analysis and polygenic scores
are generated through secondary data analysis often.
So there's definitely I think,
this pointed out to us a huge problem
in terms of the sensitivity of this information,
but from the research side of things,
the lack of responsibility or accountability
that is expected of those researchers
who are generating those scores.
So I'll stop there and see if Annamaria has something
that she would like to add in,
and then I'll maybe say one more thing.
Annamaria Smitherman: Well, as most
[INAUDIBLE], there's a lot of that I didn't understand,
but I will say that this was something that our group
was really worried about for a variety of reasons.
So what worked or what I think shifted mindsets
or created an opening was, again,
the conversation with an ethicist
that was part of the educational part,
and then the time needed to process all the information,
to ask our questions, to have the conversations.
So education and time were really crucial
to opening up possibilities
for the members of this community, I think.
Dr. Daphne Martschenko: And the last thing
that I would just say is,
I don't think by any means we are presenting our process as,
you know, the perfect process. You know, there are limitations
that I think are part of a response
that is needed at the system's level
in terms of how we engage communities
and how we work to build the trust
that has in many instances been either not there from the start
or are irreparably damaged
because of historical exploitation of communities,
predominantly communities of color, communities
who are in lower socio-economic status brackets.
So I'll just add that point there.
Dr. Miya Whitaker: Thank you.
We appreciate, your response is so helpful.
And actually, it relates to one of the questions
that we received from an audience member
who ironically was asking about trust.
How do you build trust through community engagement?
Again, trust in some communities has been destroyed
by historical research.
And there's often communities who do not trust researchers,
or even physicians sometimes,
and so they might refuse to either participate
or refuse to share data. And so how do you fix trust?
And so before we sort of get into the nuances
of a community engaged research projects like yours,
Daphne, I thought, Dr. Gold,
when we think about community and community engagement
for your process,
there is community with the providers
that you were engaging with and associated with your project.
So how did you build trust with the partners
who were going to be using the COHERE tool?
And were there ever instances where trust was eroded
and you had to build it back?
And then we'll flip back over to Daphne
to kind of talk about her community engaged projects
and the differences there,
because I think it's so important to have
that both be in the space.
Dr. Rachel Gold: Yeah, sure, of course,
as I said, we went through a year-long process
working with a diverse set of CHC staff.
Now, these are staff-facing tools.
So, and again, because of budget limitations,
we were limited to working with CHC staff
rather than patients. Obviously, that's something
we'd want to think about in the future,
but these are tools that are in the EHR,
and so they're being seen by staff.
You know, I don't think so.
I don't feel like there was an issue of trust, you know,
because the way we set up our work
was we looked at hypertension diabetes guidelines
and said, well, here are the kinds of adjustments
that are sort of recommended in these guidelines.
Which of these would you want to see in the EHR?
So we thought about the content, and then we said,
is there anything that's missing?
And then we said, what would you like?
And then when would you like these tools to show up?
Which stage in a primary care workflow?
And we really just- they totally drove what we did,
where I felt like there was-
I mean, it wasn't exactly tension,
but something that was interesting for us
was sometimes they had different points of view.
Like, we had some folks-
and some was because they had different clinics,
so for example, once someone on our committee said,
"Well, I'd really like for you to recommend
that they have the tools, recommend a certain-
that we go to, you know, a certain pharmacy where we know
they can provide home delivery." But other folks that said,
"Well, wait a minute, no, no, our clinic has a pharmacy,
and we make some of our income on that pharmacy,
and we have to have our scripts go to our pharmacy,
because that would affect our clinic's income
if you send it to an outside pharmacy."
So we had to figure out how to make that work.
And sometimes what we came up with was,
okay, well, we'll let- you know, when we work
[INAUDIBLE], we let them choose.
You know, we'd say if we can't come up
with something that's happy for everybody,
maybe part of what happens in the tools
is there are three parts that you opt into.
And that's why in one of my slides,
I showed where there were several elements of the tools
were going to trial, that provide an option,
because we know that different clinics
have different workflows,
different, you know, just rules. And so we need to-
But I did not feel like there was an issue of losing trust,
we listened to what they said,
and then we would come back to them a couple months later
and say, "Is this what you meant?"
And they would either say yes or no.
And then we come back until they said,
"Yeah, that's what we wanted you to do."
Now, what was interesting was that
the exact same folks who developed-
who were in the development committee
as in the pilot clinics, but there was some overlap.
And what was interesting was that sometimes folks who told us
they wanted stuff in the tools,
when they trialed them didn't use them,
even though it's exactly what they said.
So, you know, that's doing real-world
health services research.
And we weren't that surprised, just kind of bemused by it.
But the trust, I think it's a different dynamic when you-
and I'm sure, you know, Dr. Martschenko,
you would obviously, you know, speak far better to this than I.
But when you're working with clinic staff as researchers,
it's the different dynamic than patients.
You know, the power differential is different.
So I would turn it to you, Dr. Martschenko.
Dr. Daphne Martschenko: Thank you so much, Dr. Gold.
Yeah, I mean, I think that one of the first things
that I would say
is I think acknowledging the legitimacy of the mistrust
that communities have is an important starting point.
The other thing that I would say
is that with community engaged research,
this centers attention on the community
rather than individual research participants
as is the common practice of,
you know, IRBs, institutional review boards,
and community engaged research is focusing on the harms
caused and potential benefits to a specific community.
So as someone who's in the world of bioethics,
one of the things we talked about
is the limitations of informed consent processes,
the limitations of institutional review boards,
but how often because of the regulatory landscape
that we have,
we see those mechanisms as the kind of safeguards
that are going to catch everything.
And, you know, IRBs, informed consent,
those are not what are going to play a role
in building trust between communities and researchers,
between communities and clinicians.
And so I think it's really important to recognize
that we're not going to be able to rely on
and we shouldn't rely on IRBs,
we shouldn't rely on the informed consent document
as the mechanism through which
we're going to build trust with communities
or we're going to ensure that our research is socially
and ethically responsible.
So I think, one, recognizing, you know,
that mistrust is legitimate, that mistrust is real.
And I was seeing, you know, in the Slido
that people were also mentioning
the great work that community health workers do,
the great work that people
who come from the same cultural background and language
that also understand the community struggle play,
and I think that that's absolutely right on the mark.
It's so, so important.
And then the final thing that I would say is,
I think we need to recognize the way
that the research enterprise has been structured to,
one, focus on the harms to research participants
rather than the broader social harms
that might be a byproduct of research that's done.
And so there's also a need for us to recognize
at a more systems level the ways in which we perpetuate mistrust
by kind of ignoring the broader downstream
social implications of the work that we do.
Dr. Miya Whitaker: Thank you.
Well said. We have a question from another attendee,
who states American Indian and Alaska Native data
is often scarce in our public health surveillance systems,
and with respect to community engagement and empowerment,
how do you dismantle structural racism
embedded in any of the data systems
that can lead to the erasure of indigenous communities' data
collection analysis and reporting?
Any thoughts from either of you on that?
And I think it's an interesting question to think about
with respect to development of EHR tools or whatever,
are there cultural tailoring processes
that need to be thought of or conceptualized
when we go about collecting that data
with that particular population? So, thoughts?
Dr. Rachel Gold: I'm just going to own I do
not have expertise in this area.
It's a very challenging question.
My only thought would be, you know, again,
and I'm working again on care team facing interventions,
not patient facing directly, supporting the care team.
But if we were going to be developing these tools
for a setting that was primarily serving indigenous
or native populations, I would want to talk-
I would want their teams to tell us
how to fix them to make them a better fit.
I mean, that's a bit kind of obvious,
but I mean, that's the best I got for you.
I'm sure Dr. Martschenko has a better answer
Dr. Miya Whitaker: And great points, Dr. Gold.
It seems like there's the potential for a partnership
with you and Indian health service facilities
to kind of begin to think about how to develop
or an application for the COHERE tool.
And Dr. Martschenko,
did you want to give us a last comment on this piece
before we transition into the breakout session?
Dr. Daphne Martschenko: Well, I would just echo
Dr. Gold's point and emphasizing that, you know,
I do not personally work with tribal communities.
I'm not an expert in there.
I would want to point people in the direction
of The Native BioData Consortium.
I think they're doing really outstanding work
to ensure that tribal communities have ownership
and say in how their specimens, their samples,
are used and for what purposes,
and I really think it kind of upsets
the existing dynamic in which, you know,
the kind of traditional models for researchers
to try and recruit people for their studies
and to have already come in with their idea of what it is
that they want to do, and it's very one directional,
The Native BioData Consortium kind of flips that model,
and that researchers have to come to the community members
and propose the research that they want to do
and be in dialogue with them.
So I would just refer people to that resource.
Dr. Miya Whitaker: Thank you so much to you both,
you have taken us through some really amazing content
and provided lots of food for thought for our next session.
We are now going to transition into our breakout sessions.
So we invite all of our attendees
to at the bottom of the screen,
select which breakout session you'd like to attend.
And we will join you momentarily to begin additional dialogue
on the content from today. And thank you so much again
to our panelists' important presentations,
we so appreciate you sharing your work today.
We as a community have experienced such a fantastic day
of sharing of knowledge and perspectives,
and we've witnessed case studies and discussed
how one might apply lessons learned via breakout sessions.
We encourage you all to continue efforts
to integrate social and structural determinants
of health measures into your work.
Now, to conclude what was a truly rich event,
we will have closing remarks from doctors
Janine Austin Clayton and Karen Parker.
Dr. Clayton is the Associate Director
for Research on Women's Health
and the Director of the Office of Research
on Women's Health here at NIH.
Dr. Clayton is the architect of the NIH policy
requiring scientists to consider sex
as a biological variable across the research spectrum,
part of NIH's initiative to enhance reproducibility
through rigor and transparency.
As the co-chair of the NIH Working Group
on Women in Biomedical Careers, Dr.
Clayton also leads NIH's efforts
to advance women in science careers.
Dr. Karen Parker is the Director of the Sexual
and Gender Minority Research Office here at the NIH.
Dr. Parker serves as the co-chair of the Trans NIH
Sexual and Gender Minority Research Coordinating Committee,
the NIH SGM Research Working Group
of the Council of Councils, and the NIH
Office of the Director Equity Council.
She also serves as the executive director
of the Department of Health and Human Services, LGBTQIA+
Coordinating Committee.
Thank you, Doctors Clayton and Parker
for your contributions to NIH.
I now turn the virtual podium over to Dr. Clayton.
Dr. Janine Austin Clayton: Thank you so much, Dr. Whitaker.
You're hearing me okay? Awesome.
If somebody could bring up my slides, that would be great.
So we are coming down the homestretch.
This is an amazing workshop.
I want to acknowledge all the organizers
and the speakers and the session chairs,
the organizing committee in particular,
and of course NIMHD for leading these efforts.
And we were thrilled at ORWH to be part of this workshop.
And we're coming down to the homestretch,
and my closing remarks,
I'm asking you to think about asking more questions
to initiate change. Next slide.
So I'm going to highlight a couple of disparities
in the context of the health of women.
And if you click next, you're going to see disparities
here on this slide in terms of maternal health.
And you don't have to be able to read those small print
on those slides to see that across the United States,
we see varying situations by state.
And in the middle there is a security index,
and that's an index that looks at economic security, safety,
numbers of police reports, for example, food insecurity,
opportunities for economic growth,
and you can see variations across the United States.
And I'll just give you a couple of stats.
Over half of mothers to be in the United States
are experiencing food insecurity.
Over half of deliveries are occurring under Medicaid,
and over half of newborns-
about half of newborns are using WIC.
And at the bottom you can see maternal mortality.
Again, you don't need to be able
to read those states at the bottom.
The national average is in that horizontal line.
The blue dots are maternal mortality for white women.
The black dots- I mean, the red dots are for black women.
And you can see in nearly every state there's a disparity.
And click again for me, if you would.
We also see here in the healthcare affordability
is the more affordable the healthcare is,
the lower the maternal mortality rate by state.
And so here, just some of the differences across the US.
Next.
Here at ORWH, our mission is to expand women's
health research across NIH.
We work with all 27 institutes and centers to do that.
We want to make sure women in underrepresented racial
and ethnic groups are included in NIH support,
the clinical research and advance women
in biomedical careers. Click next.
And we imagine a world where sex and gender are integrated
across the biomedical research
continuum from those laboratory studies,
those animal models of human disease
through translation, clinical disease,
applied research, policy as well.
And we want to make sure that every woman receives
evidence-based diagnostics, treatment and care,
tailored to her own circumstances, goals, and needs,
and a world where all women in science careers
reach their full potential.
Next slide, please.
The way that we do that is through several frameworks.
The multidimensional framework that we put forward
on behalf of NIH in the current NIH-wide strategic plan
for women's health research emphasizes the fact
that we need to consider internal factors like sex
as a biological variable, genetic factors as well,
and external factors like social context,
gender as a social construct,
policies, environmental exposures,
environmental toxicants,
violence that someone may experience in environment,
and also all of that in the context of the life course.
The life course approach is germane to women's health.
It's absolutely critical to understanding
the health of women, realizing what has happened
before affects what happens in the future.
And I really enjoy Dr. Collins's presentation
and his discussion of epigenetics
and how profound it is to affect the health of women in that
you will affect the health of every person in society
by improving the health of women.
And I wish that we could be able to move that forward
in a more definitive way
so that we could accelerate progress for everyone.
One of the ways we hope to do that
is through the next NIH strategic plan
for women's health research. We have an RFI out right now.
You should have your voice heard, speak up,
let us know what's important to you
so that it can be included in the next NIH strategic plan.
Next slide, please.
So we could think about the old way of doing things
or the new way of doing things,
and I would put forward to you this ask,
how do we ask the right questions for change?
Because so much of what we end up doing
is in response to a question that's being asked,
and that question needs to be asked by us as researchers,
and we need to be listening to those communities
most affected by the diseases under study
and hear from them from the outset.
And we heard those concepts loud and clear
in many presentations today. Next slide please.
I do want to share with you the fact that we have,
for the first time ever in this country,
a national strategy on gender equity and equality.
This administration has put this front and center.
"It is imperative to encourage new generations
of scientists and clinicians to take women's health
seriously by stressing the need to step up
and make the world a healthier,
more equitable place for everyone."
And this is through policies and programs
that the federal government has established.
ORWH is leading a sex
and gender and intersectionality innovations collaborative
that is well-aligned with the national strategy
on gender equity and equality.
And it's summarized in that publication here.
Next slide, please.
Folks in Congress are also very concerned
about the health of women and asked us to,
on behalf of NIH,
host a women's health conference and look at several key issues.
And I will just cut to the chase
because we are at the end of the day.
And the URL is provided here for the meeting proceedings
and the full report,
"Perspectives on Advancing NIH Research to Inform
and Improve the Health of Women."
Some of the key cross-cutting themes that we're-
this group, our advisory council led the charge here,
were urgently needed our implementing best practices
so that their evidence-based
and evidence from people like the individuals
to whom the evidence is being applied
and a more holistic person-centered approach,
the importance of addressing care inequities,
especially among populations of women
with overlapping identities,
highlighting the disparities and pregnancy-related
and pregnancy-associated deaths in the United States,
which are far higher in black,
Alaskan native and American Indian individuals
compared to other pregnant persons.
Lower socioeconomic status and education
are also risk factors for multimorbidity
for women across their life course.
And that factor was also included
in several presentations today
when there was an effective education
and where there wasn't an effective education.
And an approach that is more intentional
where we focus on the health of women
using a life course perspective
and consider female-specific topics
such as recognizing that pregnancy
is a stress test that portends future health
of that woman and future generations.
Our over reliance on the past, on male animal models,
men in clinical trials, have left knowledge gaps,
and despite the sex as a biological variable policy,
we know there are knowledge gaps in basic science
and that informed translation.
Next slide please.
So what did we do at ORWH
in terms of trying to break down these structural barriers?
And next? Next.
And so here are just two programs
that I just want to highlight for you.
It's our privilege to lead the understudied,
underreported and underrepresented populations,
or U3 populations of women,
funding opportunity announcement and collaboration
with multiple institutes and centers.
We're looking at the influence of sex
and or gender at the intersection of sex,
race, and ethnicity
and other social determinants of health and disease
to fund research specifically focused on these populations.
And in fact, this is NIH's only funding opportunity announcement
that focuses on this particular set of populations.
You can take a look at our lecture series at the URL there
and just a few of the topics that are currently under study.
In addition, I'm privileged to co-chair along
with four other ICO directors,
the ComPASS Initiative and NIH Common Fund Program.
ComPASS stands for Community Partnerships
to Advance Science for Society,
and ComPASS is looking at enabling communities
and researchers to actually co-create research
together as partners,
true partners in all phases of the research process
to enhance the quality of interventions
so that they're community driven,
advancing health equity.
And there's information available
now and letters of intent
are due on November 18th from community organizations.
So please help us get the word out about this program
where the overall goals are to develop,
share, and evaluate community-led
health equity structural interventions
that leverage partnerships across multiple sectors
to reduce health disparities
and to help develop a new health equity research model
for community-led multi-sector structural interventions
across NIH, including other federal agencies.
So we've got technical assistance webinars
coming up in October. Please join us for those.
Next.
We're also pleased to be partnering with several ICOs
on a workshop that we are leading called Gender
and Health Impacts of Structural Sexism,
Gender Norms, Relational Power Dynamics,
and Gender Inequities,
that will be happening on Wednesday, October 26th.
So please join us for that. And my last slide, next please.
I'm just going to highlight ways to follow ORWH,
to sign up for a quarterly publication,
follow us on social media, get information about our events
and take a look at our interprofessional health
education page, our e-learning page,
where we have lots of courses available.
And I want to end by again, thanking the meeting organizers,
recognizing all the speakers from today, the session chairs,
and all of you who've taken time to attend today
and those behind the scenes
making everything work for us out in front.
Thank you so much, it's been my privilege
to be here with you today.
And I'll turn it back over to the meeting organizers
and Dr. Karen Parker.
Dr. Miya Whitaker: Thank you, Dr. Clayton.
We so appreciate your closing remarks.
And now Dr. Parker, the virtual podium is now yours.
Please deliver your closing remarks, and thank you.
Dr. Karen Parker: Thank you so much, Dr. Whitaker.
As was mentioned, my name is Karen Parker
and my pronouns are she and her,
and I serve as Director of the NIH
Sexual and Gender Minority Research Office.
And I also serve on the Social Determinants
of Health Executive Committee here at NIH.
I'd like to begin by thanking the organizers of this event
for inviting me to provide closing remarks today.
And I'm so happy to be here
among so many esteemed colleagues and participants.
First, I hope that you leave here today
with an understanding of the high level of enthusiasm
for and commitment
to social determinants of health research here at NIH.
As I listened throughout the day,
four themes really stood out to me.
And of course, I love alliteration,
so the first theme was collaboration and community.
The second is commitment.
The third is complexity and complication,
and the fourth is consequences.
So I'm just going to give a very broad overview
of some of the key points that I heard today
that I would like to lift up
as we move beyond this excellent event
back into our normal work lives. So first, I'm going to talk
about collaboration and community.
And we know that collaborations and connections with communities
are the key to broadening our understanding
of social determinants of health.
We've heard about the need for more team science
and how critical it is to reach beyond our professional silos
to those in other fields,
particularly those in the social sciences,
to ensure that we are considering all aspects
of social determinants of health
that are impacting the outcomes of our studies.
Go beyond your field to collaborate
with others who have different expertise.
We heard about even with more control for the environment,
we can learn from research in non-human primates
to better understand social isolation,
social status, and social advantage in humans.
The importance of including in a meaningful
and not perfunctory way community members in this work
was stressed.
Going beyond shallow engagement to community empowerment
was highlighted.
This includes respecting values of participants,
building trust and empowering those traditionally left
out of decision-making processes.
We were reminded that data is local
and that it requires
participants' knowledge of all uses.
In addition, the need for engagement
at the very beginning of any research process is critical.
We heard about projects working to develop data standards
and to test and validate standardized
social determinants of health data for clinical use
and how collaboration around this standardization is key.
We also learned about new collaborations
that aim to provide standardized community level
social determinants of health data
from multiple public sources.
We were reminded that words are powerful
and that collaborating around taxonomies
and language is critical.
This can range from how we talk about health equity
to the distinct differences between sex and gender.
We then learned more specifically about
how the integration of structural
and social determinants of health
at the neighborhood level and with tools such as GIS
can help us measure structural discrimination.
Next, I'd like to talk about commitment.
We know that this work will take commitment
from people across all sectors for very long periods of time
in order to really make a difference.
We heard about the need to measure neighborhood
and policy factors that impact health
and how influencers of health, such as education,
can take decades or generations to see change.
This takes commitment to the work
and to think about what can be done now
with what we currently know.
We were reminded that we must go upstream
to look at power and social values.
We must consider our systems, laws, policies,
norms, practices, and beliefs that shape our society.
This can include things like entrenched discrimination
in education,
housing and lending, to highlight just a few.
Research-examining laws and policies
that aim to erase the visibility of sexual
and gender minorities and political power
that reinforces disenfranchisement
of many different populations
must not fall to the side due to the immense commitment
it takes to do longitudinal studies
that examine these upstream contexts
across many generations.
We also heard about commitment to people,
including community members. We heard about mentoring
the next generation of researchers and providers
and the need to further fund more training.
Next, I'll talk about complexity and complication.
We know that this work is both complex and complicated.
We heard about the complicated and important process
of communicating social risk in order to increase adjustments
in the clinical setting.
Speakers discussed the issues surrounding data privacy,
genetic information,
integrating sensitive questions into care models,
and the ethics of data use in sharing,
shallow engagement, and the risks of inaction.
We were encouraged to rethink how we gain consent
and to shift our understanding of consent
to that of a living document
and embrace the concept of a re-consent
as a part of a dynamic and not static process.
We heard about challenges related to data and measurement.
This includes data related to American Indian
and Alaska native populations, those living in rural settings,
and members of the LGBTQI+ community.
Socioeconomic status commonly measured is multifaceted
and goes well beyond income.
Figuring out how to best measure concepts such as SES,
social capital, wealth,
and other related concepts is complex and complicated work.
And finally, consequences.
So what are the consequences of not doing this work?
The most urgent consequence is needless suffering
due to our inability to truly understand
the upstream social determinants of health.
However, we were reminded by our keynote speaker
that no one can study an entire causal pathway
but that it is important to measure what we can
and to always ask what we might be missing.
What is missing needs to be communicated in discussions
about our limitations and our conclusions.
In our opening remarks,
you heard from both NINR and NIMHD leadership
about some of the amazing work happening at NIH
focused on social determinants of health.
I'd like to wrap up my remarks
by highlighting one more example of work at the agency
focused on better understanding populations
experiencing health disparities.
We know that data is critical and that valid
and reliable measures are needed to illuminate disparities
in order to develop interventions and programs
to improve health.
The sexual and gender minority research office,
along with 18 other components of NIH,
recently commissioned the National Academies of Science,
Engineering and Medicine
to develop a consensus study on measuring sex,
gender identity, and sexual orientation.
This report released in March is the most comprehensive review
to date of measures of sex, gender identity,
and sexual orientation.
And it summarizes the current evidence-based
surrounding these measures for those working to advance
sexual and gender minority health and wellbeing.
It highlights key opportunities that the SGM research community
can leverage to advance representation
and further our understanding of SGM communities.
This report will serve as an integral foundation
for increasing the appropriate usage
of sexual orientation and gender identity measures
among other basic demographic questions in research,
clinical settings, and in administrative forms,
as well as enhancing our knowledge base
in variations and sex characteristics.
I encourage you to check out the report to learn more.
As a reminder, the four themes that I am taking with me
from today are: collaboration and community, commitment,
complexity and complication, and consequences.
Participating in today's event has been such a treat for me.
I have learned so much and am reinvigorated in my commitment
to increasing social determinants
of health research at NIH.
I hope that you too can return to your work
with a new sense of commitment and purpose.
As a reminder, this event was recorded
and will be publicly available in the coming weeks.
An email will be sent to all registrants
when the recording is posted.
I'd like to thank you again for attending today.
Your active participation and thoughtful questions
helped make this inaugural event a huge success.
Enjoy the rest of your day.