My husband and I are expecting our first child right around the new year.
After the initial excitement of learning that we were pregnant, we eagerly set out to learn everything we could to help ensure a healthy pregnancy and delivery. What we learned–or didn’t learn–surprised us.
More than once in this pregnancy I stood at my bathroom medicine cabinet, often in the wee hours of the night, with some unpleasant combination of headache, fever, and nausea, and wondered: “Can I take this pill? Is it worth the risk to my baby?” These aren’t easy answers–what I found online and received from my care team was ambiguous and hard to decipher, given the underlying lack of data. There aren’t easy answers because for decades pregnant women have been systematically excluded from clinical trials, leading to huge gaps in medical knowledge. As a result, millions of pregnant women world-wide face these questions.
In an age of robotic surgery, 3D-printed prosthetics and gene sequencing, no one could tell us whether it was safe for a pregnant woman to take common asthma medications, for instance.
Information Gaps and Pregnancy
There is no shortage of advice available on how to stay healthy during pregnancy, and how to deliver a healthy child. But there is a shocking lack of medical evidence underpinning much of it.
Among the gaps in knowledge–all of which are still up for debate–are:
And the gaps in our knowledge don’t end there. Risks to human pregnancy were “undetermined” in 98 percent of prescription drugs approved between 2000 and 2010, despite the fact that of the 6 million women in the United States who are pregnant each year, 90 percent take at least one medication. As we began to understand the lack of medical evidence regarding the health of pregnant mothers, we were no longer surprised when we learned that–in 2019–researchers have only just begun collecting information on the health risks facing the 1 million or more pregnant women who have a disability.
How We Got Here
There’s a reason we know surprisingly little about health risks during pregnancy. In our healthcare system, most of our knowledge comes from data that is collected during clinical trials for new drugs, biological products and medical devices.
For decades, pregnant women have been excluded from these trials out of fear that experimental treatments could harm a fetus. This fear is not always unfounded, with the most notorious example being thalidomide, a mild sleeping pill administered to pregnant women that resulted in thousands of children born with malformed limbs. This means there’s more than a 50-year gap in medical knowledge about pregnancy.
The tide is beginning to shift now. Last year, the FDA released guidance on when it is appropriate to include pregnant women in clinical trials for new drugs and devices.
This means that as we move forward, researchers will begin collecting data that will be useful for better understanding pregnancy and health risks.
This change is both welcome and long overdue. But what about the 50-year gap in our knowledge?
A Plan for Filling in the Gap
Traditional clinical trials involve bringing large groups of people together, which means expending considerable time, effort and money.
By contrast, gathering “real world evidence”–the data about patients generated during care delivery–is much less expensive. This is why there is considerable excitement about the ability of RWE to better tailor treatments to individual patients and get promising new technologies onto the market with fewer costs and delays.
Going beyond clinical data and using RWE gives researchers the ability to design a study protocol on paper, and then “assemble” massive cohorts of trial participants without requiring any travel or other logistics.
If we want to determine whether or not a pregnant woman can safely take a common asthma medication, for example, we can take the following steps to conduct a trial using real world evidence:
With access to all of the available RWE in the United States spanning the last 50 years, a study like this could involve de-identified data from tens of thousands of pregnant women.
A study like this could be run in less than a year for a few million dollars. By contrast, conducting traditional clinical trials of that size can easily cost tens or hundreds of millions of dollars and take years.
Real world evidence (RWE) could help us determine:
These are the kinds of questions that lead to more insights on whether or not there are safety risks associated with certain medications. And would definitely help pregnant women rest easier when considering whether and which meds to take.
And while there are rich debates about the standard of proof in RWE trials (e.g., did the medication cause this problem, or was the problem caused by a range of factors that make it more likely that someone would take the medication), RWE trials can both improve our knowledge base about the health of pregnant women and help researchers running traditional clinical trials focus their efforts on the most critical health risks.
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Emily Oster put it nicely: pregnant women expect better. We need to learn more about pregnancy and health risks, and gaining clinical data on pregnant women will move the ball forward.
But because we are already 50 years behind, we need to do more than move forward. Making use of RWE can help us make up for lost time.
Special thanks to Emily Oster for her insightful work in her book Expecting Better: Why the Conventional Pregnancy Wisdom is Wrong and What You Really Need to Know, and to Bob Borek and Timothy Hay for their help drafting this piece.
Pregnant Women Expect Better: Real-World Evidence Can Solve the 50-Year Knowledge Gap for… was originally published in Datavant on Medium, where people are continuing the conversation by highlighting and responding to this story.
AnalyticsIQ, a marketing data and analytics company, recently adopted Datavant’s state de-identification process to enhance the privacy of its SDOH datasets. By undergoing this privacy analysis prior to linking its data with other datasets, AnalyticsIQ has taken an extra step that could contribute to a more efficient Expert Determination (which is required when its data is linked with others in Datavant’s ecosystem).
AnalyticsIQ’s decision to adopt state de-identification standards underscores the importance of privacy in the data ecosystem. By addressing privacy challenges head-on, AnalyticsIQ and similar partners are poised to lead clinical research forward, providing datasets that are not only compliant with privacy requirements, but also ready for seamless integration into larger datasets.
"Stakeholders across the industry are seeking swift, secure access to high-quality, privacy-compliant SDOH data to drive efficiencies and improve patient outcomes,” says Christine Lee, head of health strategy and partnerships at AnalyticsIQ.
“By collaborating with Datavant to proactively perform state de-identification and Expert Determination on our consumer dataset, we help minimize potentially time-consuming steps upfront and enable partners to leverage actionable insights when they need them most. This approach underscores our commitment to supporting healthcare innovation while upholding the highest standards of privacy and compliance."
As the regulatory landscape continues to evolve, Datavant’s state de-identification product offers an innovative tool for privacy officers and data custodians alike. By addressing both state-specific and HIPAA requirements, companies can stay ahead of regulatory demands and build trust across data partners and end-users. For life sciences organizations, this can lead to faster, more reliable access to the datasets they need to drive research and innovation while supporting high privacy standards.
As life sciences companies increasingly rely on SDOH data to drive insights, the need for privacy-preserving solutions grows. Data ecosystems like Datavant’s, which link real-world datasets while safeguarding privacy, are critical to driving innovation in healthcare. By integrating state de-identified SDOH data, life sciences can gain a more comprehensive view of patient populations, uncover social factors that impact health outcomes, and ultimately guide clinical research that improves health.
Both payers and providers are increasingly utilizing SDOH data to enhance care delivery and improve health equity. By incorporating SDOH data into their strategies, both groups aim to deliver more personalized care, address disparities, and better understand the social factors affecting patient outcomes.
Payers increasingly leverage SDOH data to meet health equity requirements and enhance care delivery:
Payers’ consideration of SDOH underscores their commitment to improving health equity, delivering targeted care, and addressing disparities for vulnerable populations.
Capital District Physicians’ Health Plan (CDPHP) incorporated SDOH, partnering with Papa, to combat loneliness and isolation in older adults, families, and other vulnerable populations. CDPHP aimed to address:
By integrating SDOH data, CDPHP enhanced their services to deliver comprehensive care for its Medicare Advantage members.
Value-based care organizations face challenges in fully understanding their patient panels. SDOH data significantly assists providers to address these challenges and improve patient care. Here are some examples of how:
By leveraging SDOH data, providers gain a more comprehensive understanding of their patient population, leading to more targeted and personalized care interventions.
While accessing SDOH data offers significant advantages, challenges can arise from:
To overcome these challenges, providers must have robust data integration strategies, standardization efforts, and access to health data ecosystems to ensure comprehensive and timely access to SDOH data.
With Datavant, healthcare organizations are securely accessing SDOH data, and further enhancing the efficiency of their datasets through state de-identification capabilities - empowering stakeholders across the industry to make data-driven decisions that drive care forward.
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