Executive Summary: Who Level Funded Serves and Who It Fails: The Coverage Map and Its Gaps
LFP-06.SYN — The Populations#
The pattern of who level funded serves and who it fails maps to five design assumptions embedded in the architecture of the model. Where all five hold, the model works well. Where any assumption fails, coverage degrades in predictable, population-specific ways. Where multiple assumptions fail simultaneously, the degradation is compounding. The failures are not implementation problems. They are design consequences.
The five assumptions are: stable employment sustained across the plan year; sufficient group size to support stop loss underwriting at standard terms; cost sharing affordable relative to member income; network proximity to participating providers; and health status within the range the stop loss carrier priced for. These assumptions were reasonable for the population level funded was originally designed for — the stable, full-time employee of a small professional services firm in a metro area, without conditions that would trigger underwriting concerns.
Each population the series examined traces a specific failure. For the 55-to-64 cohort (06.02), the group size assumption fails: businesses formed by this cohort typically have 2 to 5 employees, below the stop loss underwriting threshold, while chronic condition prevalence reaches 72% (CDC NHIS) and per-capita costs run 1.8 to 2.2 times the 25-to-34 baseline (Milliman). For fractional and portfolio workers (06.03), the employment assumption fails entirely: 7.4% of all employed Americans are independent contractors on their main job (BLS Contingent Worker Supplement), with no single employer to sponsor coverage. For low-wage workers (06.04), the cost-sharing assumption fails: a $2,575 deductible equals 7.4% of gross income for a home health aide earning $34,900, placing them in clinical underinsurance before a single claim is filed. For workers with chronic conditions (06.05), the health status assumption fails through the laser: the plan must cover the member, the stop loss carrier can exclude that member from specific protection, and the employer absorbs the catastrophic exposure the product was supposed to prevent. For mental health access (06.06), both network proximity and compliance fail: Milliman’s 2019 research found patients 5.7 times more likely to use out-of-network behavioral health providers, and DOL’s Reports to Congress established that none of the NQTL comparative analyses initially submitted to EBSA demonstrated parity compliance. For rural and limited-English-proficiency members (06.07), network proximity fails geographically and the language assumption fails for a workforce where 26.4% of home health workers speak a language other than English at home. For high-turnover workers (06.08), stable employment fails: PHI National documents 77% annual turnover among home health aides and BLS JOLTS shows leisure and hospitality separations at approximately double the all-private-industry rate. For undocumented workers (06.09), the coverage architecture fails entirely: Pew Research Center’s 2025 report counts 10 million unauthorized workers in the U.S. labor force, at 15% of construction workforces and 14% of agriculture. For dependents (06.10), the cost-sharing assumption fails for low-wage employees who cannot afford the family premium increment, and adverse selection concentrates health complexity in enrolled dependents beyond what employee claims experience predicts.
The most vulnerable populations are those for whom multiple assumptions fail simultaneously. A low-wage home health worker in a rural county with type 2 diabetes and limited English proficiency faces the cost-sharing failure, network failure, language barrier, and health status pressure together — each amplifying the others.
The model has not changed. The population has. The industries driving level funded growth in 2025 employ workforces that diverge from the design assumptions in specific, measurable ways. Subsequent series must either design products that address the assumption failures or define with precision the populations for whom the standard model remains appropriate.