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.
The Cost of Food Insecurity Hiding in Young, Healthy Populations
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.”
Social Risk Data Turns Headwinds into Tailwinds
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.
Our social risk data can help identify which of those people are currently eligible but not receiving SNAP benefits. This immediately addresses a large sector of clinical needs of that at-risk population as a low-cost, high impact intervention for risk-bearing entities.
SNAP is only the first step, though. While these programs are foundational, they're typically not sufficient on their own. That’s where Socially Determined’s data comes into play, helping you concretely differentiate the drivers behind food insecurity at an individual member level. That means the right solution, at the right cost, for the maximum impact on outcomes and savings.
For example, stratified by social risk factors and clinical need, individuals facing food insecurity can receive low-cost nutrition support like fresh food boxes, only moving on to the precise, clinically vulnerable beneficiaries where a higher cost option like medically tailored meals are specifically needed and most impactful. At that point, costs are understood, measurable against impact, and allow for a more sustainable investment that maximizes outcomes while controlling costs.
As a bonus to the above plan: another partner of ours found that patient engagement leapt an astonishing 32% when enrolled in nutrition support programs.
Finally, there’s the bigger picture issue of disenrollment through shifting requirements and strained budgets. Changes to work requirements, reporting thresholds, and administrative processes can contribute to enrollment churn, particularly among working adults navigating income volatility. The social factors influencing that enrollment are then readily identified; those at risk can be stratified based on need and provided the tools they need to re-enroll, stabilizing rosters.
Additionally, those high-risk members have gone from a cloud of uncertainty for payers and providers to seeing better outcomes through non-clinical barriers being addressed, which in turn controls costs.
Finally, to the larger point of many of the policy directives that are impacting payers, providers, and their covered lives, focused work can be done through those interventions to move people away from needing that direct help Effectively, the interventions themselves create a better opportunity to move off SNAP. As a result, organizations continue to meet the goals of the program and the administration, growing the financial ability to support the next tier of at-risk populations.
Social risk data isn’t just nice to have. It’s not just a health equity tool. It’s not charity or a handout. It’s a “must have.” It addresses the previously unaddressable, it shows you everything you’re missing. Put more directly:
Social risk intelligence is the missing layer in your healthcare performance engine.