(upbeat music)
- Hi, everyone, my name is Rachel Mayer
and I'm the principal investigator
for the MITRE Maternal Mortality Interactive Dashboard
or 3MID.
My research team and I are hoping to design a tool
to provide to policymakers
to empower them to make data-driven decisions
to then understand their populations,
and select interventions that best meet the needs
of those populations
to then effectively reduce maternal mortality rates
within their state.
Currently in the US, a woman is more likely to suffer
from a pregnancy-related death
than any other industrialized nation in the world.
According to the CDC, this potential risk
of a woman dying during, immediately after,
or up to one year post pregnancy has not decreased
in the last 25 years.
This is a significant public health concern.
There are many reasons for this potential increase,
including increased rates of comorbidities
including chronic illness, site of care, access to care,
racial and ethnic disparities,
increased rate of maternal age
and other social factors like income and housing.
There are many leading causes
of pregnancy-related deaths in the US.
This includes cardiovascular and coronary conditions,
hemorrhage, infection, cardiomyopathy,
preeclampsia, and eclampsia and embolism.
As you can see on this chart,
these conditions do affect different races
at different rates.
On this chart, you can see non-Hispanic white women
and non-Hispanic black women.
Those are the two races in which we'll be focusing on
in this project.
Currently in the US,
non-Hispanic black women are three to four times more likely
to suffer from a pregnancy related death than white women.
Literature states that structural racism
and inherent bias are contributing
to these racial and ethnic disparities.
Specifically non-Hispanic black women have increased rates
of cardiomyopathy, hypertensive disorders
of pregnancy and hemorrhage.
There's also a number of different social determinants
of health that are contributing
to these increased rates of racial and ethnic disparities.
For example, non-Hispanic black women are more likely
to give birth at lower quality hospitals,
which is thought to contribute to their increased ethnic
and racial disparities
in maternal mortality and morbidity rates.
There's a number of interventions
that are currently in place in the US to try
to reduce these racial and ethnic disparities.
Currently, medical schools and different hospitals
are introducing implicit bias trainings
within their facilities.
There also are a number of bills currently on the Hill
to try and address some of these racial inequities.
It's too soon to truly know
if these interventions will be successful
but the maternal health community is hopeful
that it will have some impact.
There are a number of different data quality issues
that are present within the maternal mortality space,
starting with national statistics,
the process is as follows.
So when a woman dies,
there are a number of different individuals
that could in fact fill out her death certificate.
This includes a medical student, a physician, or a coroner,
these individuals have different levels of training
to actually in fact, accurately fill out
that death certificate.
So what they would do is list on the death certificate
the specific condition of that woman
and if it was in fact obstetric,
which is a very difficult judgment call to make,
based on the condition alone.
Then they would also check if the woman was pregnant,
not pregnant, and that is specifically broken down more
into if she was pregnant within one to 42 days,
or 43 days to one year.
The National Center for Health Statistics or NCHS,
out of the CDC would assign ICD-10 codes
to the woman based on the condition that was listed
on her death certificate.
They would then review and release this information
to the public at their discretion.
As you can see, this process is very long and complicated
and there's lots of room for potential errors.
At the same time, but not necessarily in parallel to,
many states have maternal mortality review committees
or MMRCs, these MMRCs are a group
of interdisciplinary experts that come together
to determine the causality and preventability
of a woman's death.
Preventability means that an intervention
could have been put in place to in fact prevent
that woman from dying.
Currently in the US, the CDC states
that around 60% of deaths are in fact preventable.
However, different states are using different definitions
to actually define preventability
and so this makes it very difficult
to do those apples to apples comparison among states.
These MMRCs are also determining rates
of pregnancy-associated versus pregnancy-related death.
An example of pregnancy-associated death is
if a woman while pregnant gets into a car accident,
and suffers fatal injuries from that car accident,
not necessarily because she was pregnant.
However, that pregnancy checkbox would have been marked
because she was pregnant during her death.
Pregnancy-related deaths is an example of that would be
if a woman went into labor
and suffered from obstetric hemorrhage
and died because of that hemorrhage.
She died because of a condition
that was specifically related to her pregnancy.
So I've gone through a lot of issues here,
one being that the team of experts across states
for these MMRCs do vary.
The definition of preventability can vary across states,
different MMRCs are looking at rates
of pregnancy-associated deaths.
Some are looking at rates of pregnancy-related deaths,
and in fact, some states are using different definitions
of what is classified as a maternal death.
So some of them are counting deaths
within that one to 42 day window
and some are classifying deaths between that one year,
I mean one day to one year window.
So there's a lot of issues here
and it makes it very difficult to understand
the current state of maternal mortality in the US.
For the last two years, my MITRE research team and I,
have been collating all available data resources related
to maternal mortality.
We've been sorting
through these different data quality issues
to find the best data sources out there
to best understand the current state
of maternal mortality in the US.
By collecting these data sources,
we created a synthetic population,
which is a reproduced representation of a population,
in this case, it's women of childbearing ages,
between 14 and 44 years of age.
With this, we hope to replicate
the statistical variable distributions
of a larger population.
A synthetic population is important to use in this case,
because there's only around 1000 women per year
in the US that suffer from pregnancy-related deaths.
With such a small number,
there are privacy concerns with identifying these women.
So by using a synthetic population,
we're allowed to analyze and study the factors
that surround these women
without having to worry about any privacy concerns.
With the synthetic population, we created
the MITRE Maternal Mortality Interactive Dashboard or 3MID.
With 3MID which is a policy or a micro-simulation,
the user can analyze the impact of budget spent
on maternal care, social determinants of health
and different maternal care programming
and its impact on reducing maternal mortality rates.
This specific maternal care programming was designed
by the California Maternal Quality Care Collaborative.
With the introduction of these of this programming
or quality improvement toolkits, CMQCC was able to reduce
the maternal mortality rates within their state
by 55% in the seven-year period.
This is pretty incredible when you think about the fact
that rates are increasing everywhere else in the country.
So the general premise
is that you would take these interventions
that were designed by California
and apply them to other states in the US,
would it have the same impact?
Would you be able to reduce maternal mortality rates
at the same percent as California?
So in this case, users can use these interventions
and apply them to their populations
to run different scenarios
to see the impact of these interventions on their state.
As you can see, in this picture,
the user has chosen California.
They can then choose budget spent on maternal care
and deploy different quality improvement toolkits
that address the leading causes of pregnancy-related death,
like hemorrhage or preeclampsia.
Then the user can run the simulation.
The outputs of the simulation include projected number
of maternal deaths, projected program costs,
as well as maternal mortality risk assessment scores,
on the individual level.
We use a linear regression model,
calculating the woman's individual risk based on age, race,
income, employment and county of residence.
By using this tool,
users can understand their populations,
select the best interventions
that serve the needs of their populations,
and then run different simulations
to see the impact of these interventions
on their populations.
We hope that this tool can be used by policymakers
to empower data-driven decision making
and reduce maternal mortality rates
within their particular state.
3MID is interactive in nature,
meaning that you can view different levels of the tool
at your discretion.
So when zooming in on the tool
and looking at the county level,
and so in this case, it's Glenn County,
the user can see the rate of maternal disorders
within that particular county as well as
if the county is classified as a maternity care desert
by the March of Dimes.
This is important to know because the location of
where a woman resides impacts her specific health outcomes.
So when scrolling in further into the dashboard,
you can actually look
at the individual-level demographic view.
So all of these dots that are represented
on the map represent women,
either non-Hispanic black women or non-Hispanic white women.
When selecting on a green dot,
you can see that this woman is black.
You can see her age, race, income and employment
and that individual risk score.
By understanding the populations better,
we hope to empower policy makers
to select the best interventions
to reduce maternal mortality rates within their state.
This is only a snapshot in time
of what 3MID looks like right now.
We are continually developing and evolving 3MID
to add more data and more variables, more factors.
As of now we're working on adding hospital quality,
type of insurance, number of past pregnancies,
C-section rates, and various social determinants of health.
We have high hopes that this tool, 3MID will be used
by policymakers to empower data-driven decision making,
based on demographic factors within their populations
and given resources.
We hope that this tool can be used
to reduce maternal mortality rates within the state
and contribute to the reduction
of maternal mortality in the US as a whole, thank you.
(upbeat music)