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Updated: May 27, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Public program crowd-out of private coverage.

Gestur Davidson¹, Lynn A Blewett, Kathleen Theide Call

¹University of Minnesota School of Public Health.

The Synthesis Project. Research Synthesis Report

|November 5, 2011

Summary

This summary is machine-generated.

Public insurance expansions like Medicaid and SCHIP increased coverage, but led to some crowd-out. Crowd-out, where individuals switch from private to public insurance, is hard to precisely estimate and reduce.

Related Experiment Videos

Last Updated: May 27, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

Health Services Research
Public Health Policy
Health Economics

Background:

Public insurance programs, including Medicaid and the State Children's Health Insurance Program (SCHIP), have expanded coverage.
Crowd-out, defined as a reduction in private insurance due to public program availability, is a concern with these expansions.

Purpose of the Study:

To synthesize existing evidence on the extent of crowd-out associated with public insurance expansions.
To examine strategies for reducing crowd-out in public insurance programs.

Main Methods:

This study is a synthesis of existing research on insurance crowd-out.
Analysis considers variations in crowd-out based on program type, methodology, and data.

Main Results:

Estimates of crowd-out are imprecise and vary significantly.
Crowd-out is more prevalent in family-focused programs and among higher-income families (above 200% Federal Poverty Level).
Implemented strategies like waiting periods and cost-sharing have limitations and potential drawbacks.

Conclusions:

The precise impact of public insurance expansions on private coverage (crowd-out) is difficult to quantify.
Reducing crowd-out is challenging and may inadvertently decrease enrollment among the uninsured.
Further research is needed to balance coverage goals with minimizing crowd-out.