Project 4 – The Spillover Effects of Medicare Advantage

Katherine Baicker, PhD, principal investigator (Harvard School of Public Health)

The Medicare program consists of two distinct components: (1)
traditional Medicare (TM), a government-administered fee-for-service
insurance plan with a legislatively-defined benefit structure,
administered prices, and few utilization controls; and (2) Medicare
Advantage (MA), a program of competing private health plans that may
offer varying levels of benefits and utilize various cost-containment
and quality-improvement strategies. The MA program gives beneficiaries
choice of health insurance plans. It was hoped that this private
competition would result in more efficient plans that provided Medicare
beneficiaries with higher-quality, higher-value care. 

Because the same health care providers generally serve both MA and TM
patients, changes in care induced by the MA program may “spill over” to
care delivered to TM enrollees – and, indeed, to all patients. The
ramifications of MA incentives may thus be felt throughout the health
care system if, for example, they affect standards of care or hospital
investment. Previous research in other contexts, such as the spread of
commercial managed care plans in the 1990s, suggests that these
spillovers may be substantial, but there is little research as yet on
spillovers from MA plans. Any spillover effects of MA plans to others’
spending or outcomes have direct implications for designing an efficient
MA program. Gauging the magnitude of such spillovers and establishing
causal connections requires careful empirical research to isolate causal
pathways. Other components of this project address the enrollment
decisions of Medicare beneficiaries (Project 1), MA plan responses to
changes in MA payment rates (Project 2), and the role of integration and
risk selection in MA (Project 3).  This project examines the effect of
changes in the MA sector induced by MA payment changes on the care
received by other patients via three specific Aims:

Aim 1
Examine the effect of changes in MA penetration on
spending and patterns of utilization by beneficiaries in TM and by the
privately insured.  This Aim will test the hypotheses that increased MA
penetration leads to a decrease in total spending by TM and commercial
enrollees and a convergence of utilization patterns between MA enrollees
and TM and commercial enrollees.  We will evaluate various components
of spending including inpatient and outpatient utilization for specific
conditions and utilization of particularly intensive services.

Aim 2
Evaluate whether MA penetration and utilization patterns
affect the quality of care received by TM beneficiaries and other
patients within the hospital setting. This Aim will test the hypothesis
that greater MA penetration is associated with higher-quality care for
other patients within hospitals. Greater MA penetration may promote
higher-quality care hospital-wide, but other provider reactions are
possible, including cost-shifting among patients that would dampen the
social benefits of MA penetration. The quality metrics that will be
evaluated include both process, such as use of best practices, and
outcomes, such as in-hospital mortality.

Aim 3
Investigate the effect of changes in MA penetration and plan
characteristics on quality and health outcomes of TM beneficiaries and
other insured populations beyond the hospital setting. This Aim will
test the hypothesis that greater MA penetration leads to diffusion of
high-quality care throughout the community, both through spillovers
between hospitals and through improvements to ambulatory care.
Evaluating the effect of MA enrollment on broader measures of care
received in the area, including quality of outpatient care and
longer-run mortality, will capture other potential spillover mechanisms
from those identified in Aims 1 and 2.

Supplement (Alan Zaslavsky, PhD, principal investigator)
Assess the similarity of geographic variations in utilization in TM and MA by creating indices of area MA utilization based on HEDIS utilization measures and correlating them with similar indices for TM constructed from claims as well as the End of Life Expenditure Index (EOL-EI), an index of intensity of service use in the last months of life among TM beneficiaries.  To construct these indices we will conduct area-level factor analyses using a novel statistical methodology.