Beyond Birth — A Data-Driven Look at Maternal and Child Health
By Annie Elliott, Senior Data Analyst at Metopio, and Heather Blonsky, Vice President of Data at Metopio
Why Maternal Health Deserves a Broader Lens
When we talk about maternal and child health, the focus too often stops at pregnancy and delivery. But as Annie Elliott (Senior Data Analyst) and Heather Blonsky (Vice President of Data) from Metopio recently shared, that narrow view leaves out critical parts of the story.
“It’s not just those few months when someone is pregnant and giving birth,” Elliott explains. “Maternal health is a spectrum. The data has to show what’s happening before pregnancy, during care, and after childbirth.”
From access to hospitals and specialists to economic conditions, child-care costs, and postpartum supports, maternal and child health outcomes reflect a continuum of experience and the data we track needs to match that.
Mapping Maternal Care Deserts
Data maps in Metopio reveal what many clinicians and health departments already know anecdotally: large swaths of the country, particularly across the Plains and Mountain West, qualify as maternal care deserts.
“You can see entire areas of Montana and the Dakotas where women are hundreds of miles from the nearest hospital with obstetric services,” Elliott says. “That travel distance alone has real consequences for care.”
Blonsky adds context: “It’s not just hospital closures; it’s about the entire support network that disappears with them — OB-GYNs, neonatal nurses, and specialists. Those gaps ripple through outcomes like prenatal care, maternal complications, and infant mortality.”
For public health leaders, these maps make it possible to quantify access — not as an abstract problem, but as a measurable, geographic inequity that shapes outcomes across a lifetime.
Breastfeeding Data as a “Canary in the Coal Mine”
One of the most revealing datasets the Metopio team has analyzed on this topic is breastfeeding initiation — the percentage of mothers who begin breastfeeding after birth.
“When we analyzed predictors of poor health outcomes, breastfeeding kept showing up,” says Elliott. “It’s not the breastfeeding itself, it’s what it represents.”
Blonsky agrees. “It’s a proxy for whether mothers are receiving prenatal counseling, whether they have the time and workplace support to continue, or whether they have family and community networks to help. If those supports are missing, you’ll see it reflected in a lot of other health outcomes.”
In other words, breastfeeding initiation is more than a single indicator, it’s a signal of how a community supports maternal health broadly.
The Maternal Hardship Index
To capture these layers of influence, Metopio’s team created the Maternal Hardship Index, an integrated measure that combines indicators such as:
Pre-pregnancy health
Prenatal and postpartum care access
Child-care affordability
Insurance coverage
Economic and social supports
““We wanted an index that reflected the whole journey, from a mother’s health before pregnancy to the resources she can access after birth. That’s what the Maternal Health Index offers.” ”
Elliott explains that users can build their own indices within Metopio’s analytics platform, combining multiple metrics into a single, interpretable value to visualize where hardship clusters geographically.
The Power of Sub-County Data
One of the biggest takeaways from Elliott and Blonsky’s analysis is that county-level data often hides critical disparities.
In Cook County, Illinois, for example, the county average looks moderate. But when data is mapped at the census tract level, stark inequities appear, particularly on Chicago’s South and West Sides.
“Urban areas can show bigger differences over smaller distances,” says Blonsky. “The variation between neighborhoods is often greater than the variation between counties. That’s why sub-county data is so important. It lets you see what’s really happening within communities.”
In Texas, a similar pattern emerges: when viewed at the state or metro level, maternal hardship looks manageable. But zooming into city-level data reveals neighborhoods facing higher uninsured rates, lower pre-pregnancy health indicators, and limited postpartum support.
For local agencies and hospital systems, this means resource allocation can shift from “which county needs help” to “which neighborhood needs intervention.”
Closing the Data Gaps
When analyzing maternal and child health, it's important to understand that data suppression and small sample sizes can often obscure insight. Federal sources like CDC WONDER often suppress data for privacy reasons when county-level counts are low, leaving “blind spots” across rural and tribal areas.
“Maternal mortality is a good example,” says Blonsky. “Because the numbers are small, it’s nearly impossible to get reliable, disaggregated data. So instead of waiting for perfect numbers, we go upstream to complications, uninsurance, and access indicators that are more actionable.”
To fill these gaps, Metopio often incorporates state-level data sources (like North Carolina’s fetal mortality and maternal outcomes datasets) and HRSA’s small-area estimates, which provide statistically modeled values for smaller geographies.
“State datasets are often more detailed and more current,” Elliott adds. “When a client’s needs aren’t being met by federal data, we go to the source that can answer the question.”
What the Data Tells Us
Patterns repeat across nearly every maternal and child health indicator — poverty and access remain the common denominators. But, as Blonsky emphasizes, it’s not just about poverty itself; it’s about how multiple factors compound.
“So many metrics — food deserts, transportation time, per capita income — move together,” she explains. “They are different ways of describing the same structural issue. The key is to choose the measure that best predicts the outcome you’re trying to change.”
That’s what Metopio’s data is designed for: helping decision-makers understand the why behind disparities and focus their interventions on the most predictive factors.
From Awareness to Action
Ultimately, Elliott and Blonsky’s takeaway is clear: we’re no longer in a “data desert” when it comes to maternal and child health.
““People say there isn’t enough data. But when you bring together federal, state, and small-area sources, we actually have enough to understand the problem — and start solving it.” ”
For health departments and hospitals, that means using data not just to document inequities but to drive early, upstream action before crises like maternal mortality occur.
Maternal and child health is one of the most complex, interconnected topics in community health, and one of the best illustrations of why data context matters. Access, economics, and geography all play a role, and only by layering those perspectives can we see the full picture.
“The data tells a nuanced story,” says Blonsky. “It’s about seeing how all the pieces fit together so interventions happen earlier, and outcomes improve faster.”