Gold Pattern

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Gold Pattern

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ACWW - Town Theme - ROYAL (high quality)

The impact of the affordable care act on Medicare advantage plan availability and enrollment.

THE ACA AND MEDICARE ADVANTAGE PLAN AVAILABILITY

The Patient Protection and Affordable Care Act (ACA) relied on
reductions in Medicare Advantage (MA) payment rates to finance a
significant portion of the subsidies associated with coverage expansion.
These payment reductions were controversial because they have the
potential to alter benefit generosity and the set of plan choices that
Medicare beneficiaries will face in the future. Specifically, the MA
program offers Medicare beneficiaries the option of receiving extra
benefits if they enroll in a private health insurance plan (most
commonly an HMO). In recent years, these plans have been paid at rates
higher than the estimated spending for comparable individuals in the
traditional fee-for-service (FFS) Medicare program. Because of several
legislative changes dating back to 2004, in some counties MA payment
rates and average FFS diverge significantly (Biles et al. 2009). MedPAC
reports that, on average, MA payment rates were about 13 percent higher
than FFS spending on comparable beneficiaries (Medicare Payment Advisory
Commission 2010).

These generous payments promote a wide array of plan choices for
Medicare beneficiaries (an important objective of the program, which
prior to 2003 had been called Medicare + Choice). In fact, a commonly
used diagnostic for the MA program is the availability of plans for
beneficiaries (Medicare Payment Advisory Commission 2011). When plans
exited in 1997 following the introduction of payment changes in the
Balanced Budget Act (BBA), Congress responded by increasing payment
rates to preserve plan choice. Thus, understanding the impact of payment
reform on plan choice has important ramifications for maintaining the
proposed payment rates.

The ACA has the potential to significantly alter this MA plan
landscape. Under the new law, MA payment rates will be based on spending
by FFS Medicare beneficiaries in each county. However, the generosity of
MA payment will depend on the level of FFS spending. Specifically,
counties in the lowest quartile of FFS spending will have their MA
payment rates set at 115 percent of their average FFS spending level,
those in the second quartile will have their rates set at 107.5 percent,
those in the third quartile will have their rates set at 100 percent,
and those in the highest quartile will have their rates set at 95
percent. While these changes will be implemented gradually (starting in
2012, and transitioning over a period of 2-6 years depending on the
market), insurers likely have already anticipated the impact of these
changes on their future stream of cash flows from the Medicare Advantage
products they offered before the ACA was enacted in 2010. For 97 percent
of all counties, these payment changes will result in reduced payment
generosity. Therefore, insurers may have already removed some products
from certain markets.

We investigate these issues by analyzing county-level changes
between 2010 and 2011 in the number of MA plans and contracts offered in
each county. "Contracts" measure the number of insurers
offering a given type of plan, and "plans" measure the number
of plans (and thus unique benefit packages) among which beneficiaries
can choose. From the beneficiary perspective, the diversity of choice is
largely captured by the number of plans they can choose (although plans
offered by insurers under the same contract likely share the same
provider network, so diversity of networks is better captured by the
number of contracts). Moreover, in addition to measuring diversity of
offerings, the number of contracts influences competition within the MA
market. We hypothesize that counties that are scheduled to undergo a
larger reduction in their payment rate due to the ACA rules should also
experience a larger number of plan and contract exits.

We also investigate the impact of the ACA payment changes on
beneficiary enrollment in MA plans. We are more agnostic about any
effects on enrollment; while it is possible that any reductions in plan
or contract availability may have altered MA enrollment patterns, it is
also possible that many MA enrollees shifted to the contracts and plans
that remain in place in 2011.

BACKGROUND

Although a significant amount of research has been done on the MA
program (McGuire, Newhouse, and Sinaiko 2011), there has been less work
on the effects of payment rate changes on plan or contract availability.
Cawley, Chernew, and McLaughlin (2005) investigated this question,
identifying the impact of payment on plan availability by using MA
payment changes in the BBA (which divorced MA payments from underlying
FFS costs in each county). They found that during that time period, MA
payment rates were below the level necessary to support even one MA plan
in half of all U.S. counties. Pizer, Frakt, and Feldman (2003) utilized
a similar natural experiment (payment rate changes in the Benefits
Improvement and Protection Act) to evaluate how enrollees value the
benefits offered by MA plans. Gold (2007) and Gold and colleagues (2009,
2010) have written a series of reports describing changes in the MA plan
and contract availability in the period after the Medicare Modernization
Act of 2003.

This body of work has demonstrated that the relationship between
payment rates and plan availability has continued, and that changes in
payment rates can also affect plan generosity (as measured by premiums
and the out-ofpocket costs MA enrollees face). Other recent work has
simulated the impact of payment rate changes similar to those in the ACA
on the availability of private fee-for-service (PFFS) plans in the MA
program (Frakt, Pizer, and Feldman 2009). The authors find that such
changes would lead to a dramatic reduction in the participation of these
plans. We are not aware of any research that has evaluated the impact of
the recent reforms in the ACA on MA plan availability.

METHODS

Data Sources

We used data from the Centers for Medicare and Medicaid Services
(CMS). Specifically, we obtained data on MA offerings and enrollment by
county in 2010 and 2011, benchmark payment rates by county in 2010
(Center for Medicare and Medicaid Services 2011c), and the most recent
CMS data available on FFS costs by county, from 2008 (Center for
Medicare and Medicaid Services 2011a). We also obtained data on MA plan
quality ratings at the state level (Jacobson, Neuman, and Huang 2009).

We restricted attention to counties or county equivalents in the 50
states and the District of Columbia. We dropped Loving County, Texas,
from the analysis, which had only 12 Medicare beneficiaries in 2010. Our
final analysis file contained data on 3,139 counties.

Dependent Variables

We calculated the number of MA "contracts" and plans
offered by insurers in each county in 2010 and 2011. (Using the
terminology employed by CMS, insurers enter into a contract to sell a
particular type of MA product in a given county or set of counties.
Under each of these contracts, insurers may offer multiple plans, such
as those with more or less generous benefit structures.) Each of these
dependent variables captures a different potential response by insurers
to the new payment rates. A reduction in the number of contracts would
suggest that insurers are exiting counties altogether (since most
insurers offer only a single contract for each plan type in a given
county), while a reduction in the number of plans would indicate a
reduction the diversity of each insurer's product offering in a
county.

We restricted attention to three plan types: local HMO plans, local
PPO plans, and regional PPO plans. Each of these plan types requires
insurers to set up and maintain a network of providers. PFFS plans are
also an important part of the MA landscape, and insurer offerings of
these plans were also likely affected by the ACA payment rules. However,
PFFS plan availability has also been influenced by the Medicare
Improvements for Patients and Providers Act of 2008 (MIPPA). The MIPPA
rules require that PFFS plans must use networks of contracted providers
by 2011. Developing such a network is especially difficult in many of
the markets where these plans have thrived in recent years, such as
rural counties. CMS statistics indicate that the number of PFFS plans
and enrollees declined between 2008 and 2010 as a result of these
changes; there were 1.9 million Medicare beneficiaries enrolled in 77
PFFS contracts in January 2008, compared with 560,000 beneficiaries
enrolled in 28 contracts in January 2011 (Center for Medicare and
Medicaid Services 2008, 201lb). Because the MIPPA rules may complicate
our ability to assess the impact of the PPACA on PFFS plans, our primary
analysis does not assess changes in PFFS plan availability. (We do
consider PFFS plans in our sensitivity analyses, and we control for PFFS
enrollment in our main analysis of the availability of HMO and PPO
plans.)

We also excluded plan types that are not paid on a capitated basis.
Because employer-specific plans and "special needs" plans are
not available to all Medicare beneficiaries, we excluded them from the
analysis. The final file contained 23,207 and 23,039 plan-county records
for 2010 and 2011, respectively, from which we calculated our yearly
plan counts for each county. The analogous figures for the number of
contracts were 10,313 and 10,765 in 2010 and 2011, respectively.

We also investigated the impact of the ACA payment changes on
beneficiary enrollment in MA plans using the fraction of all Medicare
beneficiaries enrolled in the set of plans described above.

Independent Variables

Our primary independent variables describe the expected generosity
of payments after implementation of the ACA. We estimated the payment
rates that will prevail under the new MA payment regime using county FFS
costs in 2008. Although the FFS-based payment rates in future years will
depend upon differential changes in FFS costs across counties during the
next several years, we are limited to these 2008 figures for the current
analysis. Because MA payment rates under the ACA are based on dividing
counties into quartiles of FFS spending, our analysis relies heavily on
these quartile categories for each county. While over time counties may
change quartiles, the current quartile is likely predictive of future
payment rates.

We also examine the impact of past generosity on changes in plan
offerings. We estimated past generosity as the difference between each
county's MA benchmark payment rate from 2010 and their FFS costs in
2008. For example, if a county's benchmark rate were $800 per
enrollee per month (leaving aside any risk adjustment for the
enrollee's health status), and if the average FFS cost level were
$700, the generosity of payment in that county would be $100.

In all analyses we control for county population (in deciles).
Because changes in PFFS offerings and enrollment may impact HMO and PPO
offerings and especially enrollment, we also control for percent of
Medicare beneficiaries in a county enrolled in a PFFS plans in 2010.
Finally, because the ACA also made MA payment changes related to plan
quality scores, we include a state-level summary of the percentage of
all contracts operating in the state with a quality rating of four or
five stars. By controlling for all of these factors, our regression
analyses provide a more precise estimate of the new ACA rules than
simple tabulations would.

Statistical Analysis

To estimate the impact of the ACA payment rate changes on MA
contract and plan offerings, we ran two versions of the following fixed
effects Poisson regression:

E([Y.sub.ct]) = exp([[alpha].sub.c] + [beta][x.sub.ct] +
[[gamma].sub.t]) (1)

In this model, we regressed the count of plans or contracts
([Y.sub.ct]) in county c and year t (2010 and 2011) on a set of county
dummies ([[alpha].sub.c]), a year dummy ([[gamma].sub.t]), and a set of
variables that vary by time and county ([x.sub.ct] all of which are
interacted with the year dummy): four quartile indicator variables (one
omitted), the 2010 MA monthly payment generosity level in dollars, the
county's overall population (in ten decile indicator variables, one
omitted), county PFFS enrollment in 2010, and state-level plan quality
ratings. The fixed effects formulation of the Poisson regression model
controls for time-invariant county-level variables and estimates the
impact of past and future payment generosity on within-county changes in
offerings.

To estimate the impact of the ACA payment rate changes on MA
enrollment, we ran the following fixed effects linear regression:

E([MA.sub.ct]) = [[alpha].sub.c] + [beta][x.sub.ct] +
[[gamma].sub.t] (2)

where [MA.sub.ct] is the fraction of total Medicare beneficiaries
in county c enrolled in an HMO or PPO plan in year t. As was the case
with the contract and plan count models, we include county fixed effects
and the same set of time-varying covariates from the models described
above. In a linear model with two time points this is equivalent to
modeling the change in enrollment in a county as a function of expected
future generosity (in quartiles), past generosity level, county
population, PFFS enrollment in 2010, and fraction of plans with high
quality ratings. In contrast to the plan and contract count models, we
ran this regression using the county number of Medicare beneficiaries as
weights.

RESULTS

In Table 1, we present the distribution of the number of plans and
contracts and MA enrollment across counties. The median county had three
contracts and six plans in both 2010 and 2011, with approximately 8
percent of all Medicare beneficiaries enrolled in an MA plan. A majority
of counties experienced little or no change in the number of plans,
contracts, or MA enrollment. It should be noted that these un-weighted
figures might understate the differences in plan availability across
quartiles. Recent work by Gold and colleagues indicates that counties in
the lowest FFS spending quartile contain only 16 percent of the Medicare
population (Gold et al. 2011). (1) The results presented in Table 1 also
indicate significant variation in generosity across counties. (2)

Tables 2a-c describe the change in the average number of plan,
contracts, and MA enrollment, respectively, by quartile and year. Table
2a suggests that, despite little change in the overall number of plans,
the new MA payment rules in ACA have influenced plan availability.
Specifically, the ACA rules encouraged plan entry in some counties and
hastened plan exit in others. Counties in the lowest quartile of
spending, where MA plans will be paid relatively generously relative to
FFS costs, saw the number of plans offered increase by 0.3 plans on
average. The average number of plans in 2010 in these counties was 5.9.
Quartile 2 counties experienced a smaller increase of 0.1 plans, and
quartile 3 counties saw a small reduction of 0.1 plans. Counties in the
highest quartile of spending, where plans will be paid less generously
relative to FFS costs, saw the largest reduction, 0.6 plans. The average
number of plans in 2010 in these counties was 9.0.

Table 2b describes a similar story for MA contracts, although in
contrast to the results for MA plans, in all quartile categories the
average number of contracts increased. In quartile 1 counties the
average number of contracts increased by 0.2 (off of a 2010 base of
2.7), while in quartile 4 counties the number of contracts increased by
only 0.1 contracts (off of a 2010 base of 3.8). Table 2c illustrates
that MA enrollment increased slightly during the study period, in the
range of 0.6 percent to 0.7 percent across all quartile groups.

Table 3 presents results from our regression models. The results
from the Poisson plan count model indicate that there is a monotonic and
positive relationship between the change in a county's number of
plans offered in 2011 versus 2010 and the generosity of MA payments
relative to FFS costs under the ACA rules. When converted from the log
scale, the parameter estimates imply that compared with the highest
spending quartile counties in the lowest, second lowest, and second
highest spending quartiles experienced increases in the number of plans
of 12.0 percent (p = .003), 7.6 percent (p = .018), and 5.4 percent (p =
.049), respectively.

The results also indicate that the level of MA payment generosity
before the introduction of the ACA rules affected the change in the
number of plans. For every 100 dollar increase in the generosity of
payment pre-ACA, the number of plans exiting rose by 2.9 percent (p =
.026). We interpret this as evidence that generous payment pre-ACA led
to more plans in the pre period, which led to a higher exit rate in the
post period at any given ACA payment level.

The analogous analysis of contracts reveals a similar pattern of
results, but the effects are smaller and are not statistically
significant. The coefficient estimates indicate that relative to the
highest spending quartile, counties in spending quartiles 1, 2, and 3
experienced increases in the number of contracts of 5.2 percent, 2.0
percent, and 1.0 percent, respectively. Pre-ACA generosity was not
associated with changes in contracts between 2011 and 2010.

The results from our analysis of changes in MA enrollment show that
compared with counties in the highest spending quartile, enrollment
increased by 0.27, 0.30, and 0.02 percentage points in quartiles 1, 2,
and 3, respectively. However, only the result for quartile 2 is
statistically significant. There is a positive and statistically
significant result for the generosity variable, implying that more
generous payment pre-ACA led to greater enrollment despite the reduction
in the number of plans.

We performed a number of sensitivity analyses to assess the
robustness of our main result on the change in the number of plans by
FFS quartile. First, we assessed whether MIPPA induced reductions
between 2010 and 2011 in PFFS plan offerings by quartile of FFS
spending, which could have offset the pattern of non-PFFS plan changes
we observed. We first analyzed the change in the number of PFFS plans as
the dependent variable. If PFFS plans were dropped in the same counties
where non-PFFS plans were added, then we should have seen a differential
change in the plan count that was exactly the opposite as the pattern of
our main result. We instead found that counties in quartiles 1 and 2 saw
relatively fewer plans exit than counties in quartile 4, a pattern
similar to our result for HMO and PPO plans. Second, we analyzed the
change in the number of all plans--HMO, PPO, and PFFS--as the dependent
variable. If new non-PFFS plans replaced PFFS plans that insurers
retired, then there should be no differences in plan changes across
quartiles. Running the regression on this new dependent variable did not
fundamentally change the magnitude or statistical significance of our
main result; only the coefficient for quartile 3 was reduced in
magnitude and statistical significance. Essentially, while it is true
that PFFS plans were exiting the market, the pattern does not indicate
that the changes offset any of the non-PFFS plan changes we have
documented.

A related concern is that the change in plans we have documented is
due to efforts on the parts of both insurers and CMS to reduce redundant
plan offerings or plans with low enrollment. To address this issue, we
performed two related sensitivity analyses. For the first analysis, we
constructed a measure of the average number of plans per contract in
each county at baseline, and added it as an independent variable to our
regression model of the change in the number of plans. For the second
analysis, we calculated the percentage of plans that were part of a
contract with four or more plans, and added it as an independent
variable. Each of these variables should capture the relative prevalence
of low enrollment or duplicate plans across the four payment quartiles.
In both of these analyses, our main result was unchanged.

One final concern with our results regarding the number of plans is
that we simply may be picking up a trend in plan availability. Perhaps
it is the case that the number of plans was already in decline, and the
rate of reduction was higher in counties with higher levels of FFS
spending. To investigate this possibility, we replicated our plan count
analysis using data from 2009 and 2010. The results do not support the
idea that there was a trend in the decline in plans. Compared with the
highest spending quartile, the number of plans in the lowest spending
quartile actually declined by 8.9 percent = .03). (3)

DISCUSSION

An important objective of the Medicare Managed Care program, in all
its incarnations, has been to promote choice. The payment methodology
prior to ACA supported this goal by setting generous payment rates. By
revising the payment system ACA ameliorated the overpayment problem. Our
results demonstrate that this led to a reduction in plan options for
beneficiaries and the analysis suggests that this may have reduced the
number of insurers offering different plan types in less generous
counties (measured by contracts). Those beneficiaries residing in
counties that offered generous MA payment pre-ACA faced larger
reductions in MA plan availability. However, we observed few changes in
the number of contracts or in MA enrollment associated with these
changes in generosity and offerings. It is likely that changes in the
number of contracts respond more slowly, as changing contracts is likely
more costly for firms. The lack of changes in enrollment suggests that
despite reduction in plan availability, enrollee demand for MA remains.
It may be the case that when payment rates actually fall, premiums and
benefits will change and enrollment changes may follow. The reduction in
plans could foreshadow future changes because plan changes reflect
anticipated future cuts as insurers prepare for the post-ACA
environment.

The normative conclusion from this finding depends on the value one
places on plan diversity. Although beneficiary choice is good,
overpaying to get that choice may not be. In addition, recent research
indicates that a smaller number of plans may be more optimal, because
the presence of too many plans causes "choice overload" and
reduces enrollment in MA (McWilliams et al. 2011). Thus, policy makers
must recognize the tradeoffs between payment generosity and other
program objectives.

Our analysis has several limitations. Most notably, the change in
MA payment generosity may have also affected the benefit generosity of
the contracts and plans that remain in place. MA plan premiums and cost
sharing amounts (e.g., the deductible level) may have increased.
Investigating this topic is complicated by the multi-dimensional nature
of plan generosity, the shifting availability of plans, and the fact
that plan generosity reflects all of the counties served, as opposed to
a single county. Thus, the investigation of the impact of payment on
plan generosity remains a topic for future research. Second, we rely on
plan anticipation of payment changes to identify effects and estimate
future generosity based on 2008 FFS costs. Firms may have more recent
and more relevant information. The actual effects on plan and contract
availability may grow as we approach the end of the transition to the
new payment system.

Despite these limitations, our analysis highlights the connection
between payment and plan diversity. Given the nation's fiscal
constraints, we cannot afford to pay more for MA beneficiaries than we
would if they enrolled in the FFS Medicare program. Yet as this analysis
demonstrates, payment reform will have ramifications beyond simply
reducing spending. Plan choices will be affected. Ultimately, this will
require policy makers to weigh fiscal needs with plan diversity.

DOI: 10.1111/j.1475-6773.2012.01426.x

ACKNOWLEDGMENTS

Joint Acknowledgment/Disclosure Statement. This research was
supported by a grant from the National Institute on Aging (grant no. P01
AG032952), and by the Marshall J. Seidman Program in Health Economics in
the Department of Health Care Policy at Harvard Medical School. We thank
James Livingston for valuable programming and data assistance. A
previous version of this article was presented at the International
Health Economics Association, 8th World Congress, Toronto, Canada, 12,
July 2011.

Disclosures: None.

REFERENCES

Biles, B., J. Pozen, and S. Guterman. 2009. The Continuing Cost of
Privatization: Extra Payments to Medicare Advantage Plans Jump to $11.4
Billion in 2009. New York: The Commonwealth Fund.

Cawley, J., M. Chernew, and C. McLaughlin. 2005. "HMO
Participation in Medicare + Choice. "Journal of Economics &
Management Strategy 14 (3): 543-74.

Center for Medicare and Medicaid Services. 2008. "Monthly
Contract Summary Report-January 2008" [accessed on June 14, 2011].
Available at: http://www.cms.gov/MCRAdvPartDEnrolData/MCESR/list.asp

Center for Medicare and Medicaid Services. 2011a. "FFS Data
Medicare Advantage-Rates & Statistics" [accessed on June 14,
2011]. Available at:

http://www.cms.gov/MedicareAdvtgSpecRateStats/05_FFS_Data.asp

Center for Medicare and Medicaid Services. 201lb. "Monthly
Contract Summary Report-January 2011" ]accessed on June 14, 2011].
Available at: http://www.cms.gov/MCRAdvPartDEnrolData/MCESR/list.asp

Center for Medicare and Medicaid Services. 2011c. "Ratebooks
& Supporting Data" [accessed on June 14, 2011]. Available at:

http://www.cms.gov/MedicareAdvtgSpecRateStats/RSD/list.asp

Frakt, A. B., S. D. Pizer, and R. Feldman. 2009. "Payment
Reduction and Medicare Private Fee-For-Service Plans." Health Care
Financing Review 30 (3): 15-24.

Gold, M.. 2007. "Private Plans in Medicare: A 2007
Update." Washington, DC: The Henry J. Kaiser Family Foundation.

Gold, M., D. Phelps, T. Neuman, and G. Jacobson. 2009.
"Medicare Advantage 2010 Data Spotlight: Plan Availability and
Premiums." Washington, DC: The Henry J. Kaiser Family Foundation.

--. 2010. "Medicare Advantage 2011 Data Spotlight: Plan
Availability and Premiums." Washington, D C: The Henry J. Kaiser
Family Foundation.

Gold, M., G. Jacobson, A. Damico, and T. Neuman. 2011.
"Medicare Advantage Enrollment Market Update." Washington, DC:
The Henry J. Kaiser Family Foundation.

Jacobson, G., T. Neuman, andJ. Huang. 2009. What's in the
Stars? Quality Ratings of Medicare Advantage Plans, 2010. Washington,
DC: The Henry J. Kaiser Family Foundation.

McGuire, T. G., J. P. Newhouse, and A. D. Sinaiko. 2011. "An
Economic History of Medicare Part C." Milbank Quarterly 89 (2):
289-332.

McWilliams, J. M., C. C. Afendulis, T. G. McGuire, and B. E.
Landon. 2011. "Complex Medicare Advantage Choices May Overwhelm
Seniors-Especially Those with Impaired Decision Making." Health
Affairs 30 (9): 1786-94.

Medicare Payment Advisory Commission. 2010. Report to the Congress:
Medicare Payment Policy. Washington, DC: MedPAC.

Medicare Payment Advisory Commission. 2011. Report to the Congress:
Medicare Payment Policy. Washington, DC: MedPAC.

Pizer, S. D., A. B. Frakt, and R. Feldman. 2003. "Payment
Policy and Inefficient Benefits in the Medicare + Choice Program."
International Journal of Health Care Finance and Economics 3 (2): 79-93.

SUPPORTING INFORMATION

Additional supporting information may be found in the online
version of this article:

Appendix S1. Author Matrix.

Please note: Wiley-Blackwell is not responsible for the content or
functionality of any supporting materials supplied by the authors. Any
queries (other than missing material) should be directed to the
corresponding author for the article.

NOTES

(1.) We thank a reviewer for making this point.

(2.) At baseline, generosity was $201 in quartile one, $132 in
quartile two, $83 in quartile three, and $28 in quartile four.

(3.) The results from these sensitivity analyses are available from
the authors upon request.

Address correspondence to Christopher C. Afendulis, Ph.D.,
Department of Health Care Policy, Harvard Medical School, 180 Longwood
Avenue, Boston, MA 02115; e-mail: afendulis@hcp.med.harvard.edu. Mary
Beth Landrum, Ph.D., and Michael E. Chernew, Ph.D., are with the
Department of Health Care Policy, Harvard Medical School, Boston, MA.

Table 1: Descriptive Statistics

                                             25th
                               Minimum    Percentile   Median

Number of plans, 2010             0          3           6
Number of plans, 2011             0          3           6
Change in plans                  -9         -1           0
Number of contracts, 2010         0          2           3
Number of contracts, 2011         0          2           3
Change in contracts              -3          0           0
MA enrollment, 2010               0          3.6%        7.7%
MA enrollment, 2011               0          3.8%        79%
Change in MA enrollment         -15.9%      -0.2%        0.4%
MA payment generosity, 2010    -489         79         137

                                  75th
                               Percentile   Maximum

Number of plans, 2010              9          55
Number of plans, 2011              9          50
Change in plans                    0           7
Number of contracts, 2010          4          27
Number of contracts, 2011          4          27
Change in contracts                0          -3
MA enrollment, 2010               14.2%       50.7%
MA enrollment, 2011               14.2%       55.5%
Change in MA enrollment            1.2%       16.0%
MA payment generosity, 2010      188         752

Note. all figures are based on data from HMO and PPO MA plans
only.

Source. CMS's "Medicare Options Compare" files for 2010-2011,
contract-plan-state-county enrollment data for 2010-2011, and
Ratebook data for 2010-2011.

Table 2: (a) Number of Plans by FFS Spending Quartile, 2010-2011. (b)
Number of Contracts by FFS Spending Quartile, 2010-2011. (c) MA
Enrollment by FFS Spending Quartile, 2010-2011

                                 Number of     Number of     Change,
                                Plans, 2010   Plans, 2011   2010-2011

(a)
Quartile 1 (lowest spending)        5.9           6.2          0.3
Quartile 2                          7.0           7.1          0.1
Quartile 3                          7.7           7.6         -0.1
Quartile 4 (highest spending)       9.0           8.4         -0.6
Total                               7.4           7.3         -0.1

                                 Number of     Number of
                                Contracts,    Contracts,     Change,
                                   2010          2011       2010-2011

(b)
Quartile 1 (lowest spending)        2.7           2.9          0.2
Quartile 2                          3.2           3.4          0.2
Quartile 3                          3.4           3.5          0.1
Quartile 4 (highest spending)       3.8           3.9          0.1
Total                               3.3           3.4          0.1

                                    MA            MA
                                Enrollment,   Enrollment,    Change,
                                   2010          2011       2010-2011

(c)
Quartile 1 (lowest spending)       17.3%         17.9%        0.6%
Quartile 2                         15.0%         15.7%        0.7%
Quartile 3                         15.2%         15.8%        0.6%
Quartile 4 (highest spending)      17.7%         18.4%        0.7%
Total                              16.5%         17.2%        0.7%

Note. all figures are based on data from HMO and PPO MA plans
only. Plan and contract counts are unweighted; enrollment amounts
are weighted by Medicare population.

Source: CMS's "Medicare Options Compare" files for 2010-2011,
contract-plan-state-county enrollment data for 2010-2011, and
Ratebook data for 2010-2011.

Table 3: Regression Results

                  Change in         Change in
                  the Number        the Number        Change in
                   of Plans        of Contacts       Enrollment
                 (log scale)       (log scale)

Quartile 1
  (lowest
  spending)    0.114 ** (0.038)    0.050 (0.055)      0.27 (0.27)
Quartile 2      0.073 * (0.031)    0.020 (0.045)   0.30% * (0.15)
Quartile 3      0.053 * (0.027)    0.010 (0.040)     0.02% (0.12)
Generosity
  pre-PPACA
  ($100)       -0.030 * (0.013)   -0.001 (0.019)   0.17% * (0.07)

* p < .05.
** p < .01.

Note. Regression model also includes county population dummies,
PFFS penetration, and percent of plans with high quality ratings.
All figures are based on data from HMO and PPO MA plans only.
Plan and contract regressions are unweighted, enrollment
regressions are weighted by Medicare population.

Source. CMS's "Medicare Options Compare" files for 2010-2011 and
contract-plan-state-county enrollment data for 2010-2011.

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