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Executive Summary
E. Kathleen Adams, Mark R. Meiners, and Brian O.
Burwell
See, E. Kathleen Adams, Mark R. Meiners, and Brian O. Burwell,
A Synthesis and Critique of Studies on Medicaid Asset
Spenddown, January 1992; The full report is also available from the DALTCP website (http://aspe.hhs.gov/daltcp/home.htm)
or http://aspe.hhs.gov/daltcp/reports/syncri.htm to go
directly to it.
Asset spend-down in nursing homes is the process by which individuals
enter nursing homes as private pay clients, deplete their available assets in
paying for their care, and then enroll in the Medicaid program once they are
impoverished. "Medicaid asset spend-down" is a source of considerable concern
to disabled elderly persons who face the prospect of extended nursing home
care. It is also a matter of considerable concern to State Medicaid programs,
since Medicaid indirectly serves as a safety net for middle and upper class
individuals who incur catastrophic nursing home costs.
Numerous studies of Medicaid spend-down have been conducted in the last
five years, and much has been learned. This paper summarizes the results of
recent research, with a particular emphasis on how research methods used in
these studies have affected results. Not only has much been learned about the
phenomenon of Medicaid spend-down itself; researchers have also come to
recognize key relationships between research methods and estimates of asset
spend-down.
Two Measures of Medicaid Spend-Down
Two different measures of Medicaid asset spend-down have been developed
by researchers, each of which is informative, but which provide different
perspectives on the problem. The first measure (Spend-Down I) examines
spend-down from an "insurance" or "risk" perspective. It measures the
percentage of persons originally admitted to nursing homes as private payers
who eventually convert to Medicaid prior to final discharge. Spend-down I
is therefore a measure of the risk to individuals of spending down to Medicaid
over the course of their lifetimes, given the probability they enter a nursing
home as private payers.
The second measure of Medicaid spend-down (Spend-Down II) measures
the percentage of Medicaid recipients in nursing homes who were not eligible
for Medicaid when they were originally admitted. Spend-Down II is useful in
capturing the proportion of State Medicaid expenditures for nursing home care
which is accounted for by those who spend-down.
Summary of Estimates
Several studies report widely varying estimates of these spend-down
measures, based on several national and State level databases. The critical
factor explaining differences among these studies is the length of time that
persons are studied. The proportion of persons spending down during a single
stay is much lower than the proportion of persons who spend-down over their
entire lifetime, probably because half or more of these people have multiple
stays. In general, studies using national data tend to show lower estimates of
spend-down than do State studies because the latter data bases tend to observe
people over longer time intervals.
Based on the studies conducted to date, it appears that somewhere
between one in four and one in five persons who originally enter nursing homes
as private payers convert to Medicaid before final discharge (Spend-Down
I). Although there is close agreement between comparable national and State
studies on Spend-Down I, there are not enough State studies to determine the
extent to which spend-down rates vary from State to State.
On the other hand, estimates of Spend-Down II vary considerably
across States, no doubt reflecting variations in Medicaid eligibility policies
across States as well as other factors. For example, major studies are
available for Michigan (27%), Wisconsin (31%), and Connecticut (39% to
45%). However, most national studies of Spend-Down II give lower figures,
reflecting the shorter time periods that they cover.
Other Findings
In addition to estimates of Spend-Down I and Spend-Down II, other
aspects of Medicaid spend-down have also been examined. One is the length of
time it takes for people to spend-down to Medicaid after nursing home
admission. On this question, the research is more consistent on the
median time to spend-down than the mean. The results of existing
studies are fairly consistent in reporting that of those people who
spend-down, the majority spend-down within a year of nursing home
admission. This finding suggests that most people who spend-down have
limited assets when they first enter a nursing home, less than the cost of one
year of care--about $32,000 in 1991. The research is less consistent in
estimates of the mean time to spend-down, since means are disproportionately
affected by the relatively few persons with extremely long lengths of stay
prior to converting to Medicaid.
Studies in Connecticut, Michigan and Wisconsin also show that people who
spend-down to Medicaid spend more time on Medicaid after converting to
Medicaid coverage than they spend as private payers prior to conversion.
The studies suggest that Medicaid-paid days account for at least 65-75 percent
of all nursing home days used by those who spend-down. However, the research
also shows that, once eligible for Medicaid, people who spend-down pay a
greater proportion of total nursing home costs, through ongoing income
contributions, than persons who are eligible for Medicaid at initial admission.
Thus, spend-downers account for a somewhat lower percentage of total Medicaid
expenditures than their percentage of Medicaid-covered nursing home days.
There is some evidence that females who enter nursing homes are at
higher risk of spending down to Medicaid than males. This may be related to the
fact that females admitted to nursing homes are less likely to be married, are
less likely to be discharged alive, and have longer lengths of stay, an
average, than males. Females may also have fewer available assets at nursing
home admission than males, and thus spend down more quickly, other factors
being equal. However, no research has directly tested this hypothesis.
Some studies have tried to estimate the out-of-pocket costs of privately
paid care prior to conversion to Medicaid among people who spend-down. These
estimates range widely, and are very dependent upon assumptions about the costs
of private care, which are not available in any of the data sources used in the
studies conducted to date. As the median and mean time to
spend-down differ markedly, so does the median and mean cost of privately paid
care prior to conversion among those who spend-down.
Directions for Future Research
Researchers now recognize several important relationships between
research methods and estimates of Medicaid spend-down. First and foremost,
researchers now know that it is extremely important to use longitudinal data
bases that are able to track nursing home use by individuals over multiple
nursing home admissions. There is a strong correlation between multiple
admission, lifetime length of stay, and the likelihood of spending down. This
finding has been borne out by the data. For example, the Connecticut data show
that over 75 percent of all persons who spend-down to Medicaid have more than
one nursing home admission.
Other important relationships between research methods and estimates of
Medicaid spend-down include:
- The sample population must be appropriate to the Spend-Down measure
being estimated.
- Estimates of Spend-Down I and Spend-Down II are also dependent upon
the distribution of payment sources among all nursing home admissions.
- Estimates of Spend-Down I and Spend-Down II are improved with the
length of time covered by the data.
- In addition to having data on sources of payment for individuals in
nursing homes, it is important to have Medicaid enrollment data,
preferably from Medicaid administrative data systems.
In sum, the research conducted to date has shown the definitive
advantages of superior data sets in deriving more reliable estimates of
Medicaid spend-down.
Much of the best research on Medicaid spend-down has been conducted on
nursing home users in the State of Connecticut, since the best available data
source for conducting spend-down research exists there. More spend-down
research needs to be conducted in other States, but this, of course, must be
preceded by the construction of comparable "all-payer" longitudinal data sets
of nursing home users in these States. Spend-down research conducted from
cross-sectional survey data will always encounter substantial data limitations,
no matter how assiduously these surveys attempt to collect retrospective data
on sampled members.
Work on the Connecticut data set should also continue, with more focused
analyses of Medicaid spend-down, Including changes in spend-down rates over
time, the magnitude of out-of-pocket costs prior to Medicaid conversion, and
the differential characteristics of persons who spend-down and those who remain
private payers throughout their nursing home stays.
Finally, more research needs to be conducted on exactly what goes on
during the process of Medicaid spend-down. There is speculation that in
addition to paying for private nursing home care, many people spend-down
purposefully by divesting or sheltering their assets. Similarly, better data is
needed on other out-of-pocket expenses of nursing home users (e.g., expenses
for acute care services and prescription drugs) which may accelerate their
falls to impoverishment.
The policy relevance of Medicaid spend-down research is a better
understanding of the number, characteristics and circumstances of people who
experience high out-of-pocket costs for private nursing home care, forcing them
into impoverishment and reliance on public assistance under Medicaid. This
information is important in the consideration of strategies to build public
and/or private risk pools that will help to mitigate the catastrophic potential
of nursing home costs for future users. While much has been learned about
Medicaid asset spend-down in the last few years, there is still much that
remains unknown, or at least uncertain. Further research on Medicaid spend-down
should continue to inform the policy process as alternative strategies for
mitigating its impacts are debated and, hopefully, implemented.
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