You made the growth call, and on paper, it was the right one. The market looked strong. Employer density was solid, demographics were favorable, and competition was manageable. You invested in sales, contracting, and marketing, and the enrollment followed. The number came in.
Then a few months into the plan year the claims start arriving, and the population you grew into doesn’t behave like the one the enrollment number described. Condition burden runs heavier, utilization runs higher, and the economics of that growth are suddenly harder to manage than the volume ever suggested. The decision wasn’t wrong. It was made from the right inputs for the wrong question. Enrollment counts tell you how many members you added. They can’t tell you whether the market you grew into carries a cost profile you should have priced, contracted, and planned around before you committed a dollar.
The tension is familiar to anyone who has carried a growth number and a loss-ratio target at the same time. A market looks attractive, you invest, enrollment follows. Then the medical cost trend data starts coming in, and the picture changes.
Earlier in this series, we made the case for reading a market at the county level rather than the state average. That’s where demand, demographics, and competitor presence diverge in ways a statewide number hides. But those signals all answer the same question: where your market is and who’s in it. None of them tells you what that market will cost to cover. That’s the signal we haven’t covered yet, and it’s the one that lands directly in your claims: condition burden.
The mechanism is simple. Condition burden shifts a market’s economics long before it surfaces in the volume story. Concentrate chronic conditions in a population with limited access to primary or specialty care, and utilization climbs in ways enrollment counts never flag. The variation isn’t marginal: CDC data showed diagnosed diabetes rates ranged from 4.4% to 18.6% across U.S. counties in 2023—a more than fourfold gap. The growth decision was incomplete: the population’s cost profile wasn’t visible before the resources were committed.
The question was never whether a market can grow. It’s whether the mix of condition burden, provider supply, and utilization makes that growth defensible against the pricing, network capacity, and plans you’ve already locked in.
Statewide averages are the most common proxy for market opportunity and the least useful for the decisions that matter most. Take a common chronic condition like COPD. A state can post a single, moderate-looking prevalence rate while its counties swing from well below that average to well above it: the difference between a manageable chronic-care load and a concentration that reshapes utilization, network demand, and cost. A plan sizing that market on the state figure sees one number. The plan year is written by the county.
Prevalence is only the first signal, and on its own, it still misleads. Utilization separates a county where high prevalence turns into active, ongoing care from one where nominal burden overstates real demand. Provider supply indicates whether that demand can be met at all: as of mid-2026, about 108.5 million people live in a federally designated primary-care shortage area, where clinicians meet just under half of the estimated need. More than three in five of those designations are rural: often the same places where chronic-condition burden runs highest.
Put it together, and the point is sharp. High prevalence against adequate supply is a market you can serve. The same prevalence against a thin, concentrated provider base is a very different market. But when looking at a statewide summary, the two look identical.
To size a new market, most plans start with a generic market report: demand and population figures that growth, network, and pricing each pull separately, from disconnected sources that no single view ties together. But the read that actually decides cost—condition burden set against provider supply and utilization at the county level—is exactly what a generic market report can’t give you. It shows demand without indicating where the burden of conditions concentrates, or it shows population size without determining whether the access infrastructure can accommodate growth. When those teams each work from their own slice of that disconnected picture, they can arrive at different views of the same geography, making it harder to align on what the market actually is.
The fragmentation behind this is well documented. In a 2025 Arcadia survey, 85% of participating plans reported they had not integrated all available data into a centralized analytics platform. A separate Milliman MedInsight survey found that only 13% of plans reported using analytics “very effectively” for total-cost-of-care management, with most citing a lack of integration as the reason. Growth and cost risk aren’t being read together because, in most plans, the view that connects them doesn’t yet exist.
Reading all three signals together at the county level produces a prioritization input, not a prediction: where to pursue product, network, or partnership action first, and where to hold spend until the conditions for sustainable growth exist.
A market with strong condition signals and adequate provider supply can support more aggressive enrollment investment. Put that same signal against limited supply, and it may need a network build-out or partnership before heavier spend makes sense. Without that distinction, growth and network teams make the same call from different assumptions, and the plan pays for the gap later.
“Every plan I’ve worked with can tell you how many members it added last year. Far fewer can tell you what those members cost to cover, and by the time the claims answer that question, the pricing’s locked and the network’s built. Growth isn’t the number you enrolled. It’s the number you can still afford twelve months later.”
You’ve probably formed the objection already: no serious plan grows on public averages alone. Fair. You have claims history, broker relationships, last year’s results, and regional leads who know their providers and employers better than any dataset. That’s real, and it usually beats a spreadsheet. Consider pausing to notice the sequence, though. That knowledge confirms or corrects a market after you’re in it: after the network is contracted and the pricing is set. The forward-looking half of the decision, the part that commits budget before the first claim lands, still rests on the market view you built up front. If that view counts members without reading the cost underneath, local expertise arrives too late to reprice the risk.
Staying with enrollment counts and statewide averages doesn’t stop growth. It makes growth harder to sustain. You spend against geographies that look attractive but carry a cost profile your pricing and network never planned for, and you miss the markets where burden and access line up with your ability to serve members and hold margin. The coordination cost is just as real: without a shared market view, teams spend time reconciling different reads instead of acting on one.
Before you commit budget to a market, ask five things:
If there’s one thing to take away from this blog, or the series as a whole, it’s:
The growth you can win and the growth you can afford are not the same thing.
These blind spots were never a failure of skill or will: the tooling to close them simply didn’t exist.
Getting the full read right means three things: trustworthy data, the right level, and the cost underneath. Only then is your number defensible: not a volume you hope holds, but a cost profile you’ve already priced.
That’s why we built Data Axle for Healthcare: a single, current, aggregated, de-identified view that brings all three together at the county level, rather than a stack of mismatched reports. So the next time you defend a growth number, it’s one that holds up when the claims arrive.
This is Part 3 of a three-part series on building a payer growth plan you can defend.
Read Part 1, “The Blind Spots Hiding in Your Data,” on why commonly used public data points are less reliable than they seem. Read Part 2, “The State Looks Flat. The Counties Aren’t.” It explains why a statewide average can’t describe where you actually compete.
Reveal a market’s true cost with one unified view of trustworthy, county-level data in Data Axle for Healthcare.
Brooke O’Keefe is a seasoned marketing strategist with a passion for blending creativity and data to drive business growth. With deep experience across both B2B and B2C brands, she brings a unique perspective to building strategies that resonate with diverse audiences and deliver measurable results. As a leader in brand, content, and go-to-market strategy, Brooke has spent her career forging strong cross-functional partnerships that connect teams, customers, and purpose. She thrives at the intersection of storytelling and technology—crafting campaigns that make brands more human and measurable.