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Updated: May 15, 2026

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
The validity of claims-based risk estimation in underinsured populations
1Institute for Health and Disability Policy Studies, University of Kansas, Lawrence, KS, USA. jhall@ku.edu
Objectives:
To demonstrate a threat to validity in using claims-based risk tools with chronically ill, underinsured populations.
Study Design:
We tracked disease burden of high-risk pool beneficiaries with potentially disabling health conditions receiving enhanced health insurance benefits through a federally funded research demonstration. At baseline, beneficiaries paid high premiums and cost sharing for risk pool coverage, and most met common criteria for underinsurance. Study benefits provided intervention group members premium and cost-sharing subsidies and additional coverage; control group members paid usual premiums and coinsurance and received usual benefits. We hypothesized that enhanced benefits for the intervention group would increase or stabilize health status measures and decrease case-mix weights, reflecting stabilized or reduced disease burden.
Methods:
The SF-12v2 health survey was used to measure health status and the Johns Hopkins Adjusted Clinical Groups (ACGs), Version 8.2 with DX-PM model and prior cost for a non-elderly population, was used to measure disease burden.
Findings:
Over a 3-year period, SF-12v2 scores showed stable health status for the intervention group and significant decline for the control group, while ACG case-mix weights, major illnesses, and chronic condition counts rose significantly for the intervention group but remained stable for the control group. Increased resource utilization for the intervention group appears to have driven increases in ACG measures.
Conclusions:
When high cost-sharing constrains access to care, risk tools that rely on medical claims may not provide an accurate measure of disease burden.
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