One of the most covered disruptions from the 2025 government shutdown was the contentious block on SNAP benefits.
Today, we’re going to offer a clear, actionable path towards stability and informed strategy. Our goal is to help you use social risk data to change uncertainty into success for payers and risk-bearing providers, delivering a measurable impact on health outcomes, costs of care, and even patient engagement.
Ready? Let’s get into it.
Even with the 43-day shutdown resolved, SNAP, alongside Medicaid, face structural revisions, eligibility adjustments, and evolving administrative requirements, impacting the lives of millions and complicating payer and provider strategy.
The effects of the prior funding disruptions, include enrollment churn, expanded work requirements, shifts in cost-sharing structures, and administrative reporting thresholds, are already influencing individual benefit qualifications, all of which is already disrupting benefit access for working adults.
Finally, for risk bearing entities, changes in eligibility and administrative burden can directly affect food access and affordability, coverage continuity, and downstream healthcare utilization patterns, creating volatility in long-term forecasting around outcomes and cost performance.
Fortunately, there’s a way to address pretty much all of it.
First, the issues themselves need to be identified and understood.
In a recent evaluation we conducted with a client on a population of more than 450,000 commercial beneficiaries, 70% of 20–29-year-olds were flagged as facing food insecurity, a social risk factor directly attributed to a cost increase of $85 PMPM above their peers. This is a traditionally healthy and lower cost segment in almost any consideration of pop health and actuarial strategy, especially because they all have jobs that provide health insurance.
That’s the “who.” Let’s get into the “how” and “why.”
Community (SDOH) and individual (HRSN) data are a must-have for VBC and commercial risk-bearing organizations. Social factors impact upwards of 80% of health outcomes, pushing cost of care alongside it.
However, the right social risk data embedded in a claims-based analytic workflows demystifies those factors. And when you know what people are up against, you can more thoughtfully prioritize and intervene.
This opens the door to create an island of stability for risk-bearing organizations to build effective strategy that is less susceptible to policy shifts.
Let’s go back to the above example: commercially insured 20-29 year olds at risk for food insecurity.
National data shows that a meaningful share of people who qualify for SNAP do not participate. It varies state to state, county to county, but USDA estimates suggest participation rates among eligible individuals typically range between 75–85%. That means roughly 1 in 5 eligible people may not be enrolled, disproportionately adults of working age without children in commercial populations like the one described above.