Discussing Your Biggest Community Health Improvement Cycle Data Topics & Priorities
What the data around public health priorities reveals — and how Metopio can help you turn those priorities into a plan
Ask any room of community health professionals what's at the top of their next assessment, and you'll hear a familiar list: maternal and child health, chronic disease, mental and behavioral health, substance use disorder, housing, food insecurity. Health departments and hospitals across the country are naming the same needs, even when they're working in very different communities.
That convergence is exactly why Metopio’s VP of Data Heather Blonsky and Data Analyst Jaelyn Braswell, who work directly with health departments and hospitals on their assessments, chose to dig into three of those shared priorities on a recent webinar: food insecurity, access to care, and maternal health. Here's what they covered.
Why "Point-to-Point Distance" Isn't Good Enough
Metopio's drive-time-to-grocery-store indicator, which measures the minimum drive time from the population-weighted center of each census tract to the nearest grocery store — built on 2024 grocery store locations from the Chain Store Guide and routing data from OpenStreetMap. Zoomed out, the differences are stark: for example, some parts of Alaska show drive times of literally days.
That routing data is the foundation of every drive-time indicator in the Metopio platform, and it matters because it's a meaningfully different measure than the one most food access data has historically relied on. The USDA's Food Atlas, last updated in 2019, calculates food access as a straight-line, point-to-point distance. As Jaelyn put it, that approach assumes a directness that doesn't exist in the real world:
““If you were to imagine, where you’re sitting now, a line to your nearest local grocery store — would you be able to walk that? The answer, most likely, is no, because there’s something in the way.” ”
In rural areas especially, roads go around mountains and back across rivers before they reach a destination a straight line would put much closer. Drive time captures that; linear distance doesn't.
Metopio's food desert indicator builds on the drive-time data by layering in income, identifying areas with both low food access and a poverty rate over 20%, and can be stratified by race and ethnicity. Heather noted that once poverty enters the picture, the geographic patterns shift — with drive time and poverty compounding heavily in parts of the Deep South, for instance, in ways that don't show up as clearly in other regions.
As Heather said in our webinar: "It's not just that drive time — it's also who doesn't have a car, who doesn't have access, who doesn't have the means to get that half mile, or half hour, away."
Closing the Gap in Hospital and Maternal Care
The same drive-time methodology extends to healthcare access, with indicators for drive time to inpatient hospitals, psychiatric hospitals, hospitals with ICUs, and hospitals with obstetric care — all built on the same OpenStreetMap routing data, with hospital locations sourced from Medicare's 2025 cost reports.
Heather zoomed into a stretch of Montana, South Dakota, and North Dakota where hospitals are sparse enough that "county has a hospital, yes or no" stops being a useful question — the real story is that can be three counties before you reach one. That distinction matters even more for maternal care, where hospitals with obstetric services are even further apart than general inpatient care.
Zooming into Michigan's census-tract-level data for another example, Heather also flagged a detail that's easy to miss in county-level views: state lines don't stop people from driving to the nearest hospital, even when that hospital is across a border. Metopio buffers 80 kilometers beyond a state's border to capture those cross-state trips — which matters for planning, since Medicaid reciprocity rules can turn a 13-minute drive into a hard stop depending on where that line falls.
The same underlying approach (proximity rather than per-capita counts within a boundary) also applies to Metopio's gun violence data, calculated as straight-line distance from an incident, since a shooting just over the county line doesn't stop mattering to the people who live near it.
Going From Insight to Intervention with the Program Library
Knowing where the gaps are is only half the equation. Metopio's program library was built to help teams move from "we have a barrier" to "here's what's worked elsewhere for a community like ours" — tagged by rurality, population, and outcome so you can filter for programs proven in contexts similar to your own.
What This Means for Your Community
Regardless of how your assessment names its priorities, the underlying question is usually the same: how far, and how hard, is it for someone in your community to reach what they need? Drive-time data (for groceries, hospitals, or maternal care) gives you a more honest answer than distance alone, and a program library built on real outcomes helps you decide what to do about it.
If you want a closer look at your own service area, county, or multi-county collaboration, we're happy to help — reach out and we can build that view for you.