Publication
Prospective Benefit Design for the Medicaid Expansion Population: The Predictive Capacity of Self-Reported Health Measures
Podcast | Presentation Materials |
Video for i-Pod | Click here to access the presentation slides. |
Flash8 Video | Click here to access the webinar transcript. |
MP3 (128 kbps) | |
MP3 (64 kbps) |
Description
State Medicaid programs are preparing to absorb a projected 16 million new enrollees in 2014, most of whom are low-income childless adults about whose health care needs relatively little is known. Without information on prior medical history for the expansion population, states are unable to design and implement innovative and effective benefit design targeting this group.
Given these circumstances, self-reported health measures collected at the time of enrollment are likely to be the only practical means of gathering data for programmatic use. Dr. Lindsey Leininger, Assistant Professor of Health Policy and Administration at the University of Illinois at Chicago, is leading a SHARE-funded project to assess Wisconsin’s pioneering use of this strategy to inform program design for childless adults enrolling in its Medicaid program, which was expanded via waiver to cover this population in 2009.
In this webinar, Dr. Leininger presents findings from this analysis of Wisconsin's experience. She augments these findings with a nationally representative analysis of a sample of childless adults drawn from the Medical Expenditure Panel Survey (MEPS). Dr. Leininger’s Wisconsin analysis provides evidence gathered “in the field,” and the MEPS analysis provides both a nationwide benchmark and an assessment of the predictive capacity of a wide range of self-reported health measures—including but not limited to those used by Wisconsin.