At the Future of Health Data Summit this Fall, economics professor and author Emily Oster began her fireside chat with Rep. Lauren Underwood (IL-14) using staggering statistics.
These maternal mortality numbers are alarming for the U.S., a nation with the highest GDP that spends more than any other country on healthcare (see Figure 1 showing US maternal mortality rates compared to other countries). Professor Oster and Representative Underwood laid out the plight of maternal death in stark numbers and human impact. Together, they championed the healthcare needs of all women, especially women of color and their babies while shedding light on the complex data landscape that must be woven together to pinpoint the risks for maternal mortality.
The barriers to improving maternal health start with gaps in data. Maternal death is not an event isolated to the birth event in the hospital. It is an event whose risk factors emerge upstream early in the pregnancy, through the birth and for a period afterwards. Medicaid, a state run program, pays for two-thirds of births to Black mothers. As a state-run program, these patients will not be found in many commercial payer data sets.
Even if those patients’ data could be collected from state Medicaid programs, some states like Kansas, South Dakota, and Tennessee did not expand Medicaid coverage beyond 60 days of the birth, and both the mother and baby lose health care coverage for postnatal care. (See Figure 2 below highlighting the status of Medicaid or state-funded family planning programs.)
Figure 2. State Decisions on Medicaid Expansion and Family Planning Programs Affect Women’s Access to Postpartum Care
Another data gap is the lack of rich social determinants of health data that includes race, housing, transportation, education, nutrition, income, and environment. Studying the extent to which each of these factors affects different cohorts of mothers is the foundation for evidence-based changes to the current care model. Importantly, data driven analysis of these factors is important to avoid stereotyping. For example, Representative Underwood underscored that even world-class athletes like Serena Williams and Alyson Felix experienced traumatic births, defying the notion that athletic, privileged Black women were exempt from the risk of death during childbirth.
In healthcare, data is useful when it is timely and refreshed continuously, however, maternal health data suffers from incredible lag. For example, the state of Illinois released their 2021 maternal mortality report using data from 2016 and 2017. The lag in maternal health data renders it nearly useless for making timely decisions in healthcare delivery, especially during a pandemic. There is also a lag in data related to substance use disorders and overdoses, which have high impact on the health outcomes of mothers and babies.
In response to this challenge, Representative Underwood and Congresswoman Alma Adams (NC-12) started the Black Maternal Health Caucus (the “BMHC”) in 2019. The BMHC has grown to over 100 bipartisan members with a goal of “elevating the Black maternal health crisis within Congress and advancing policy solutions to improve maternal health outcomes and end disparities.” The Caucus has begun to engage various participants in the healthcare community. As a result, some insurance companies have expressed interest in sharing data to support this effort.
Representative Underwood characterized other types of support needed to maintain maternal health before, during, and after their pregnancies. They include paid leave, and proper prenatal and postnatal care to support healthy behaviors and manage potential health risks. Mothers who do not have paid maternity leave are impacted the most. Professor Oster posited that paid maternity leave is one of many missing puzzle pieces that make up all the gaps in supporting the health of pregnant women and their babies.
The Family and Medical Leave Act (“FMLA”) is a labor law providing unpaid leave and ensuring job protection, but many women cannot afford to take unpaid medical leave. Representative Underwood discussed a new legislative proposal that could give workers paid family leave for 12 weeks. Her comprehensive policies include diversification of the perinatal workforce with nurse midwives, physician assistants, obstetricians, doulas and lactation consultants. These additional types of providers improve access to care, better manage health risks, and enable patients to exercise choice in their prenatal care and birth process.
Representative Underwood introduced the Black Maternal Health Momnibus Act of 2021 (a portmanteau of moms and omnibus). It is the most comprehensive legislation of its kind for maternal and child health. It attempts to close many maternal health gaps with mandates to:
With this legislation, Representative Underwood sought to address some of the biggest underlying risk factors for maternal mortality. In her home state of Illinois, the number one cause of maternal death is mental illness. Title VI of the Momnibus will help build the infrastructure to support recent mothers who need community-based treatment. Title V, supports data collection to review causal analyses of maternal mortality so that the true cause of death can be fully evaluated.
Maternal mortality varies greatly by state but today, states do not have a mechanism to share data so they can cross compare risk factors, care approaches, and outcomes. As the discussion drew to a close, Representative Underwood offered an open invitation to the healthcare technology community to engage the BMHC in identifying a solution to the fragmentation of maternal health data. She offered a hopeful concluding statement: “One of the things I know about technology companies is that if you have a culture of openness, a culture of solving problems and a culture that says let’s do something, you can do it.”
Edited by Elenee Argentinis, Head of Marketing, Datavant
Editor’s note: This post has been updated on December 2022 for accuracy and comprehensiveness.
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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