To begin, two algorithms were created that would work to link administrative records and survey data to categorize respondents' health insurance coverage as public or private. One algorithm, dubbed 'CON', classifies coverage type relying solely on a conceptual understanding of the general tendencies of the health insurance landscape. The second alternative algorithm, dubbed 'AR', was created using data from an experimental study called Comparing Health Insurance Measurement Error (CHIME) that included responses to the post-Affordable-Care-Act redesigned CPS survey matched to administrative records from a private health insurer.

The two algorithms were then compared by examining three reporting accuracy metrics of coverage type classification: underreporting, overreporting, and prevalence. Then, we used the results to assess whether the AR algorithm improved, or could improve, the accurate categorization of private versus public coverage on the CPS.

Principal Findings

This study's findings show that the AR algorithm produced measurable improvements over the CON algorithm, with only one minor trade-off. Underreporting of public coverage was reduced by about 5 percentage points, and overreporting of private, marketplace, and nongroup/marketplace coverage was reduced (by almost 2 percentage points; almost 7 percentage points; and almost 11 percentage points, respectively).

The cost of these improved metrics was higher underreporting of marketplace and nongroup/marketplace coverage.

However, because one of the main goals and motivations for this research project was to address the problem of the chronic underreporting of public coverage, researchers suggest that the trade off to improve reporting of public coverage with a cost of underreporting marketplace coverage (that has relatively low prevalence) would be acceptable.

To read the full article, visit this link.

Learn more about:

• The Medicaid Unwinding
• Current Population Survey data
• Other CHIME products