INTERNAL REVIEW DRAFT: DO NOT QUOTE

Technical Paper Series

Congressional Budget Office

Washington, DC

INCORPORATING LONGEVITY EFFECTS INTO

LONG-TERM MEDICARE

PROJECTIONS

John Sabelhaus (e-mail: johnsa@cbo.gov)

Michael Simpson(e-mail: michaels@cbo.gov)

Julie Topoleski (e-mail: julie.topoleski@cbo.gov)

Congressional Budget Office

Washington, DC

JANUARY 2004

2004-2

Technical papers in this series are preliminary and are circulated to stimulate discussion and

critical comment. These papers are not subject to CBO’s formal review and editing

processes. The analysis and conclusions expressed in them are those of the authors and

should not be interpreted as those of the Congressional Budget Office. The authors are

grateful to John Sturrock and Todd Anderson for assistance in preparing the longitudinal

Medicare data sets. References in publications should be cleared with the authors. Papers in

this series can be obtained at http://www.cbo.gov (select Publications and then Technical

Papers).
Abstract

The cost of the Medicare program will increase dramatically as the baby boom generation

reaches retirement. Results from the Congressional Budget Office Long-Term Model (CBOLT)

indicate that Medicare costs will equal more than 9 percent of GDP by 2078. This paper

considers the impact of replacing the Centers for Medicare and Medicaid Services approach of

using an age- and sex-based model with one that is also based on age and sex but adds time until

death to the forecasting model. Other researchers have found that using only age and sex can

significantly overstate future Medicare costs. Incorporating a model that uses age, sex, and time

until death into CBOLT does not alter the conclusion that the cost of the Medicare program is

going to increase significantly, but by holding projected excess cost growth constant, it does

lower predicted costs by more than 5 percent by 2078. 
1

1. Introduction

Over the next few decades, the cost of the Medicare program will increase dramatically as the

baby boom generation reaches retirement. Total Medicare expenditures accounted for 2.6

percent of GDP in 2002, and the Centers for Medicare and Medicaid Services (CMS) estimates

that by 2035 Medicare spending will account for 5.3 percent of GDP, increasing to 9.3 percent by

2076 (Board of Trustees, 2003). This paper explores an alternative to CMS’s basic methodology

to project future Medicare costs: including time until death in the forecasting model. The new

methodology does lower long-run Medicare forecasts, but it does not fundamentally change the

prediction that Medicare costs will become an increasing economic burden, assuming that current

law persists.

When projecting future Medicare costs, CMS uses trends in Medicare spending to

forecast expenditures conditional on health status in the short term. Of note here is that CMS

proxies health status by age and sex (Cutler and Sheiner, 1998). This assumption implies that

medical needs change only as the age and sex distribution of the population changes. It does not

take into account the idea that, as life expectancy improves, years of good health may also

improve. That is, there is no allowance for the possibility that, as longevity improves, the costs

associated with the end of life may be delayed until older ages. 

In the long run, CMS assumes that growth in costs per beneficiary is equal to the growth

in GDP per capita plus 1 percentage point. Note that this 1 percent excludes the impact of

demographic change in the population, which is estimated separately (Board of Trustees, 2003).
2

The alternative approach to forecasting Medicare costs introduced in this paper adds an

additional dimension to the proxy for health status: time until death. Although longevity

increases need not be associated with improved morbidity, recent research does find such a

correlation, at least with regard to the elderly (Lubitz, Cai, et al., 2003). The relationship

between age and Medicare costs can be explained largely by time until death (see for example,

Miller, 2001, and Stearns and Norton, 2002). In contrast to a model that forecasts population

only by age and sex, a model that also includes time until death allows for the high costs

associated with the final year of life to be delayed to more advanced ages as life expectancy

increases. This is what Miller (2001) refers to as the delay-of-morbidity hypothesis. Excluding

time until death can overstate forecasts of lifetime Medicare costs by as much as 16 percent by

some estimates (Stearns and Norton, 2002).

In addition to adding time until death into the forecasting model, this paper also

investigates the impact of forecasting not only aggregate Medicare costs but also category

specific costs (inpatient hospital; skilled nursing facilities or SNF; hospice; home health;

outpatient hospital; physician and other Part B utilization; and managed care) in the context of a

larger policy-simulation model. This allows for a more flexible policy-analysis tool. Further,

most research on this issue has focused solely on forecasting aggregate costs in a stand-alone

model rather than on incorporating Medicare forecasts into a broader model.

A key factor underlying this analysis is the cost index itself. When looking at cost indices

by age and sex costs increase with age, not surprisingly, for both men and women until about age

90 and then drop off slightly. In a regression framework, age and sex actually explain very little
3

of the variation in Medicare costs. When time until death is added to that framework, the

explanatory power of the covariates increases more than tenfold. Not surprisingly, costs for

individuals in the last year of life are higher than for individuals further from death at all ages. 

Costs for both men and women in the last year of life decline somewhat with age, while costs for

those surviving more than one year are relatively flat.

These cost indices are then incorporated into the Congressional Budget Office LongTerm Model (CBOLT). The model uses the assumption adopted by CMS and in prior CBO

analyses that costs per beneficiary will grow with GDP per capita plus 1 percentage point in the

long run. This 1 percentage point will be referred to as “excess cost growth” (all cost growth not

explained by changing demographics or by growth in GDP per capita). The cost indices are fixed

and do not vary over time.

Forecasting Medicare costs by using a model that includes time until death does not

change the conclusion that the cost of the Medicare program will increase significantly, nor does

it change the time frame by which it happens. Forecasts of total Medicare costs as a percentage

of GDP are the same as the CMS estimate for 2035 and are only slightly lower for 2076. 

Comparing forecasts produced by CBOLT using an age and sex index and an age, sex, and timeuntil-death (TUD) index, by 2078 the forecast produced using the age-sex model is just over 5

percent higher than the forecast using the age-sex-TUD model. The results are similar when

forecast in aggregate and by category. 
4

2. Demographic Determinants of Medical Expenditures

With the impending retirement of the baby boomers, the potential impact of demographics on

Medicare spending has received a great deal of attention from researchers. Underlying CMS

Medicare forecasts is a fixed age-sex index. However, researchers have found that age and sex

are poor indicators of health care costs. As an alternative, models have been developed that

include time until death as well as age and sex. The general conclusion has been that excluding

time until death leads to forecasts that overstate costs.

Cutler and Sheiner (1998) set out a framework for evaluating the impact of demographic

change, noting that total medical spending depends on the number of people in a given age

group, the health status of that age group, and average medical spending conditional on that

health status. Each of these factors changes over time. That the population will become

increasingly elderly is easy to predict. Forecasting the average health status of a population and

the spending conditional on that health status is more complicated.

Previous analysis has shown that health care expenditures increase quite dramatically as

death approaches, and empirical analysis performed for this paper supports that conclusion. 

Further, the literature shows that expenditures by time until death have been stable over time

(see, for example, Lubitz and Riley, 1993). Using data from the Continuous Medicare History

Sample, and restricting their sample to beneficiaries ages 65 or older and not enrolled in

Medicare managed care, Lubitz and Riley find that the proportion of Medicare payments

accounted for by decedents, defined as persons in their last year of life, fluctuated very little from

1976, when 28.2 percent of payments were for decedents through 1988, when 27.2 percent of
5

payments were for decedents. Garber, MaCurdy, and McClellan (1998) find that, despite

compositional changes in the expenditures of decedents, total expenditures accounted for by

decedents were consistent from 1988 to 1995.

The literature also shows that Medicare costs are very strongly associated with time until

death and are only weakly associated with age. Using the Continuous Medicare History Sample,

a 5 percent sample of Medicare beneficiaries, Lubitz, Beebe, and Baker (1995) analyze data for

all Medicare beneficiaries ages 65 and older who died in 1989 or 1990. They exclude from their

analysis all beneficiaries enrolled in HMOs, beneficiaries with end stage renal disease (ESRD),

and beneficiaries not continuously enrolled in both Hospital Insurance (HI) and Supplementary

Medical Insurance (SMI). Classifying Medicare costs by both age and time until death, Lubitz et

al. find that costs in the third through tenth years before death vary quite little by age. They do

note a decline in the level of payments by age in the last two years of life; that is, at advanced

ages, payments within the last two years of life decline with age.

Extrapolating from the Lubitz et al. analysis, Miller (2001) argues that age is a poor proxy

for health status and cannot be used reliably as a basis for forecasts. He argues that time until

death is a better measure of health status because, as life expectancy increases, time until death

allows for the delay in high costs associated with the end of life. Using age alone (or age and

sex) makes no such allowance and assumes that, as longevity increases, there is simply an

increase in the number of high-cost years. Because end of life costs decline with age, Miller

believes that a forecasting model that uses both age and time until death is likely to understate

future costs and that the current relationship between age and time until death is not likely to

persist into the future.
6

In using time until death as a proxy for health status, Miller assumes that the high health

costs and the period of morbidity that accompany the end of life are delayed. This contrasts with

the assumption inherent in an age-and-sex-based forecasting model, where increased longevity

simply extends the period of higher costs and morbidity near the end of life. Miller assumes that

Medicare expenditures can be decomposed into three multiplicative factors: the number of

enrollees, the average cost per enrollee, and a general price-level factor. 

The following notation is used throughout this paper:

Xt Medicare benefits in year t

B Medical inflation

Nasdt Number of Medicare enrollees in year t by age (a), sex (s), and time until death (d)

Pasd Average Medicare costs by age (a), sex (s), and time until death (d) in base year

Specifically, when using an age model, Miller expresses aggregate benefits as the following:

Similarly, for projections using time until death, the model is as follows:

Miller projects costs both by age and by time until death. He finds that, by 2070, cost

projections using the age model are 14 percent higher than projections using the time-until-death

model for beneficiaries over age 65. This assumes an increase in life expectancy to 80.9 years by
7

2070, and uses what Miller describes as the conservative longevity forecast by the Social

Security Board of Trustees.

More recent research by Stearns and Norton (2002) supports this finding. Stearns and

Norton treat the exclusion of time until death as an omitted-variables problem that biases the

estimated impact of age upward. They estimate two models: a simple model excluding time until

death and an expanded model that includes both time until death and interactions of age with

time until death. Their simple model assumes that health care expenditures are a function of age,

sex, and geographical indicators. If time until death is an omitted variable that is negatively

correlated with both health care expenditures and age, the estimated coefficient on age is biased

upward. Their solution is to add time until death to the equation, arguing that the benefits of

including time until death outweigh the problem of its potential endogeneity. Using the

Medicare Current Beneficiary Survey and a logit model, Stearns and Norton first estimate the

probability of any expenditure conditional on the covariates, for both their simple and expanded

models. They then use ordinary least squares to estimate health care expenditures conditional on

some use.

Using a split sample validation approach, Stearns and Norton find that the overall

goodness of fit is better when using the expanded model that includes dummy variables

indicating whether the person is in the first, second, third, or fourth quarter before death and the

interactions of each of these variables with age. Adding the time-until-death variables greatly

increases the explanatory power of the expenditure-level regressions. They find that using the

simple model results in estimated lifetime expenditures for people ages 66 to 70 that are 10
1

Cost information is not available for beneficiaries enrolled in managed care.

2

 Note that all major outliers (beneficiaries with more than $1 million in annual spending) have been removed from the

data set.

8

percent higher than a model that includes time until death when using current life tables, and that

this increases to 16 percent when projected life tables for 2020 are used.

3. Alternative Indices for Forecasting Total Medicare Spending

The first step in this analysis is to build a set of indices by age, sex, and time until death. For this

analysis, an index is created for total Medicare costs and for the following six categories1

: 

• Inpatient hospital,

• Skilled nursing facilities,

• Hospice,

• Home health,

• Outpatient hospital, and

• Physician and other provider service utilization.

These indices are used in forecasting costs and in constructing estimates of excess cost growth,

both historically and over the ten-year budget window. 

The indices are created using a longitudinal database that contains information on

Medicare spending for covered services used by individual beneficiaries over several years. The

database was produced by Acumen, LLC, and was derived from information recorded and

maintained by CMS for Medicare claims processing.2 The records include information on a 5

percent sample of beneficiaries enrolled in Medicare from 1989 through 1999. Beneficiaries are

retained in the data set after death, and 5 percent of new enrollees are added each month. Data
3 Note that truncating the data earlier has no impact on the average cost structure.

9

include enrollment and entitlement status (aged fee-for-service, disabled fee-for-service, aged

managed care, disabled managed care, not enrolled, or dead) for each month, dates of birth and

death, race, sex, and zip code. Monthly utilization and expenditures are also included for the

following categories: inpatient hospital, skilled nursing facility, hospice, home health,

outpatient, and physician and other provider service utilization. Only Medicare covered spending

is included here. Note that expenditures for durable medical equipment, a covered benefit, are

not available in the data set.

Unlike previous research, this analysis includes Medicare beneficiaries under the age of

65. For this analysis, only beneficiaries never enrolled in managed care are included. 

Expenditure information is not available for beneficiaries for the time in which they were

enrolled in a managed care plan, so to maintain a consistent panel, all beneficiaries ever enrolled

in Medicare managed-care plans are excluded. The monthly data are aggregated to annual totals

relative to the date of death. If a beneficiary dies on June 1, his or her final year begins on June 2

of the year preceding death and ends on his or her date of death. Annual totals relative to the

date of death are calculated for all beneficiaries. For beneficiaries who do not die within the

sample time frame, a death date of December 31, 1999 is assumed.

Although data are available through the end of 1999, for this analysis, the data set is

truncated at the end of 1997.3 Of interest here is how Medicare expenditures vary not only by

age and sex but also by time until death (TUD). Specifically, this analysis looks at expenditures

in the last year of life (TUD group 1), expenditures in the second-to-last year of life (TUD group

2), and expenditures prior to the final two years of life (TUD group 3). In order to have
10

observations for groups 2 and 3, it was necessary to truncate the data. Note that the cost indices

are by age, sex, and time until death are calculated relative to a 65 year old man with more than

two years of life remaining.

Expenditures are deflated to 1990 dollars using GDP per capita. By removing the impact

of per capita GDP growth, the coefficient on the time trend should represent, in dollar terms

excess cost growth.

Average costs by age, sex, and time until death are calculated by using fixed-effects

regressions that include a simple time trend. Note that, in all cases where age is used, it enters

linearly after age 65 and in five-year age groups for ages 20 through 64. Observations for those

age 19 and younger are grouped together to avoid small sample problems. Similarly, after age

91, observations are grouped because of the decreasing number of observations at advanced ages.

 Note that for the time trend (t) 1990 is equal to 1. 

The notation used (in addition to that described earlier) is as follows:

Dasd Dummy variable by age (a), sex (s), and time until death (d)

t Time trend with t at 1990 equal to 1

$ Coefficient on time trend

GDPt GDP per capita in year t

0t Excess cost growth rate in year t

The following set of regressions is run. 
11

This set of regression models is used to estimate average total costs and category-specific

average costs for each category listed above (inpatient hospital, SNF, hospice, home health,

outpatient hospital, and physician and other provider service utilization).

Accounting for the time trend allows for the average costs by age, sex, and time until

death to be calculated accurately regardless of the time period. Its inclusion effectively removes

the effect of changes over time. This is particularly important for certain categories of costs

where there were sharp changes in rates of growth and subsequent reform of payment systems,

for example, during the period of analysis 1989 through 1997. To test the sensitivity of the

results to the period included in the analysis, the analysis was run using a shortened time period,

and the average costs were not sensitive to the years included.

In each regression model, all coefficients are statistically significant, but there are large

differences in how much of the variation in the dependent variable is explained by the

independent variables (see Table 1). For model 1, the R-squared is 0.01, indicating that age and

sex explain very little of the variation in Medicare expenditures. The R-squared for model 2

(time until death only) is 0.13, and for model 3 (age, sex, and time until death) it is even higher at

0.15. This is consistent with the results in Stearns and Norton (2002). Age and sex alone explain

much less of the variation in annual Medicare expenditures than either time until death alone or

age, sex, and time until death combined. 

In the case of each index, annual Medicare costs increase over time with the smallest

annual increase in the age and sex case and the largest in the time-until-death case (see Table 1
12

for summary statistics). Although the estimates of excess cost growth (the coefficient on the

time trend divided by the mean of the dependent variable) over the period 1990 to 1997 are all

very similar at about 3 percent, the estimate is largest for the TUD index and smallest for the age

and sex index. Omitted from the table are the p-values on the F-test on the significance of the

fixed effects (P). In each case, the p-value is less than 0.0001. Clearly, the fixed effects are

jointly highly significant in each case.

The results are further summarized in the figures. Figure 1 summarizes the results from

Model 1 for men and women. Rather than showing average costs per beneficiary, costs have

been converted to an index where average costs for a 65-year-old male are equal to 1. As the

figure shows, average program costs increase with age. The rate of increase is about the same for

men and women, but costs for women are below costs for men after age 65. Before age 65, costs

for women are above costs for men. Note the distinct drop in the index at age 65, as healthy 65-

year-old beneficiaries enroll in Medicare.

Figures 2 and 3 show the cost indices by time until death. Figure 2 shows the results for

men, and Figure 3 shows the results for women. As the figures show, the patterns are similar for

men and women. For those who survive more than two years (TUD=3), average program costs

increase with age until declining slightly as beneficiaries surviving more than two years enter

their nineties. For men who die within 13-24 months (TUD=2), costs are relatively constant

across ages until beneficiaries reach their eighties. For women, there is a more pronounced

decline with age for TUD group 2. Lastly, looking at those within one year of death (TUD=1),

we see that average costs generally decline with age. These are consistent with the results
13

reported in Lubitz, Beebe, and Baker (1995). The declining pattern is more striking for women

than for men. As with the indices by age and sex, there is a drop at age 65, as healthy 65-yearolds become eligible for Medicare.

Looking at cost indices that vary only by time until death, costs are highest for those who

die within one year, at more than eight times higher on average than for an individual more than

two years from death over the sample period 1990 through 1997. Costs for those who die within

13 to 24 months are three times higher than for those living longer than two years. (Note that

these results are not shown graphically.) Again, this is consistent with previous research (see, for

example, Miller, 2001).

The results are similar when looking at category-specific excess cost growth rates. 

Looking at each category of cost, again the models that include age, sex, and TUD fixed effects

explain substantially more of the variation in expenditures than when only age and sex fixed

effects are included (see Table 2). The p-values on the F-tests for the significance of P are

omitted from the table. In every case, the p-value is less than 0.0001, showing that, jointly, the

Ps are highly significant. 

The coefficient on the time trend varies considerably across expenditure categories, in

1990 dollars. Looking at this coefficient relative to the mean expenditures for a given category

provides an approximation of excess cost growth. Recall that growth in GDP per capita has

already been removed from expenditures. These excess cost growth rates range from about 1

percent (the level CMS assumes long-run excess cost growth will be) to almost 20 percent. 

Estimated in this way, SNFs have the highest excess cost growth rate at almost 20 percent,

followed by hospice at almost 18 percent, and home health at 16 percent. Excess cost growth for
4

 For an introduction to the CBOLT model, see Congressional Budget Office (2001).

14

physicians and other Part B services has been close to 6 percent, and inpatient hospital close to 1

percent. Following this methodology, excess cost growth estimates for hospital outpatient are

negative over this period. Note that these are not the excess cost growth rates used in the

forecasting model. These are the excess cost growth rates for 1990 to 1997; the excess cost

growth rates used in the forecasting model are calculated using the cost indices calculated here

and the cost forecasts for 2003 through 2013.

Note that information on managed-care costs is not available from the panel data set; the

index for total Medicare costs is used as an approximation for managed care. The age-sex profile

for beneficiaries enrolling in managed care is almost indistinguishable from those not enrolling,

and so there it seems reasonable to assume that costs relative to those for a 65-year-old male will

be similar for both managed-care and fee-for-service beneficiaries.

4. Generating Long-Run Medicare Forecasts

Generating the long-run Medicare forecast required building a model that was fully integrated

into the Congressional Budget Office Long-Term Model to take advantage of the detailed

demographics and economic modules already contained within CBOLT.4 A detailed set of

Medicare calculations that incorporate these new cost indices have been added to CBOLT. 

These calculations include forecasting the number of Medicare beneficiaries both aged and

disabled. Previous research in this area focused only on elderly Medicare beneficiaries. 

Medicare costs are assumed to grow with the change in beneficiaries (Nasd) multiplied by the cost

index (Pasd) , GDP per capita, and excess cost growth(0t). Specifically, 
15

As stated earlier, excess cost growth accounts for all growth in benefits not accounted for by

demographic change and economic growth.

Developing estimates of the beneficiary population by age, sex, and time until death

involves estimating total population by time until death, developing a set of ratios of

beneficiaries to total population, and then applying those ratios to population projections. 

CBOLT uses a detailed demographics module to forecast population by age and sex , and that

population is distributed across the three time-until-death (TUD) categories. Calculating the

percentage of people expected to die within 12 months (TUD category 1), 13 to 24 months (TUD

category 2), and 25 or more months (TUD category 3) involves applying age and sex specific

cohort survival probabilities to the underlying population. In particular, the number of people

expected to die with the next 12 months is calculated as the population times one minus the

probability of surviving an additional year. For TUD group 2, the process is repeated for one

minus the probability of surviving an additional two years, then subtracting the number expected

to die within one year. The number of people in TUD category 3 is the residual after subtracting

the other two categories from the population for that age and sex. This process is repeated for

disabled worker beneficiaries to calculate the number of beneficiaries in each time-until-death

category.
5

 The model is being refined to include a separate set of cost indices for ESRD beneficiaries and to more accurately identify such

beneficiaries.

16

The next step is to calculate the number of beneficiaries for each year. Starting with

beneficiaries by age and sex group for 2002 (data provided by CMS), the ratio of beneficiaries to

population is calculated for each age and sex group for the population, ages 1 through 100. For

those ages 21 to 64, the process is different. The number of disabled worker beneficiaries (on

Social Security Disability Insurance for two or more years) is subtracted from the number of

Medicare beneficiaries, and any remaining beneficiaries are calculated as a percentage of the

population for each age, sex, and time-until-death group. Note that this remainder is a small

group and represents beneficiaries with end stage renal disease.

5

 

The calculated ratios are then applied to population (or to population by time until death)

to solve for the number of Medicare beneficiaries. In the case of population by time until death,

the same percentage of each TUD group for each age and sex group are assumed to be

beneficiaries. That is, if 95 percent of 66-year-olds are beneficiaries, 95 percent of those

assumed to die with one year, two years, and beyond are assumed to be beneficiaries. 

Beneficiaries as a percentage of the population are then scaled to match official CBO estimates

of the number of beneficiaries until the end of the ten-year budget window. These scaled

percentages are used to forecast beneficiaries after the end of the ten-year budget forecast period.

To calculate the number of beneficiaries enrolled in managed care, the Medicare panel

data set (described above) is used. Specifically, the data are used to calculate the percentage of

beneficiaries, by age and sex, who were enrolled in managed care in 1999, the last year for which

those data are available. Those percentages are applied to the calculated number of Medicare
6

 The only way an individual under the age of 20 can be eligible to become a Medicare beneficiary is because of end stage renal

disease, and ESRD beneficiaries are not allowed to enroll in Medicare managed care. (Beneficiaries enrolled in managed care

who later develop ESRD are permitted to continue their enrollment.)

7

 The results are similar when using only a time-until-death only index, but for clarity of exposition, that line has been omitted

from the chart.

17

beneficiaries, to arrive at an estimate of Medicare managed-care enrollment. Note that no

beneficiaries under the age of 20 are allowed to be enrolled in managed care.6

Excess cost growth is calculated separately for each category of spending and each index

(age-sex, age-sex-TUD, and TUD). Recall that excess cost growth refers to all growth that is not

accounted for by changes in demographics of growth in per capita GDP. In projection years 1

through 10, excess cost growth is calculated as a residual. Specifically, it is calculated such that

CBOLT projected costs match official CBO projections for each category of spending. For year

11, excess cost growth is equal to the average of the previous ten years. In years 12 through 25,

excess cost growth rates gradually approach the long-run excess cost growth rate of 1 percent. 

After year 25, excess cost growth is simply equal to the long-run rate. That is, all categories go

to 1 percent excess cost growth. This is consistent with CMS assumptions (Board of Trustees,

2003). The same long-run excess cost growth rates are used for each index.

5. Implications for Excess Cost Growth and Long-Run Forecasts 

The choice of cost index will have consequences for estimates of excess cost growth, both

historical estimates and those into the forecast period, and for Medicare cost forecasts. As Figure

4 shows, historically there has been a large amount of variation in excess cost growth regardless

of index used. The two excess cost growth series track each other very closely.7
8

These are arithmetic means.

18

Historical excess cost growth is calculated in the same way as excess cost growth over the

ten-year budget window: growth in the cost index multiplied by the beneficiary population and

growth in GDP per capita are removed from growth in benefits. Because a historical series of

beneficiary counts by age and sex was not readily available, beneficiaries were calculated using

the 2002 ratio of beneficiaries to population. As a result, some of the fluctuation in excess cost

growth is actually attributable to programmatic changes that increased the number of eligible

beneficiaries. The large excess cost growth rates for 1974 and 1975, for example, are attributable

to the addition of disabled worker and ESRD beneficiaries to the Medicare roles. The low excess

cost growth rates of the late 1990s are the result of the cost-containment measures in the

Balanced Budget Act of 1997. Although there has clearly been a lot of variability in excess cost

growth, it does appear that the degree of variability has been declining over time.

Average excess cost growth declines as the period considered begins later. Using the

age-sex-TUD model, the average from 1970 through 2002 is 2.9 percent.8

 This decreases to 2.7

percent from 1980 to 2002 and to 2.0 percent from 1990 to 2002. Indeed, for 1998 to 2002 the

average excess cost growth rate is -0.6 percent. In each of these time periods, there is

considerable variation in excess cost growth rates. In the forecast for 2004 to 2013, excess cost

growth is relatively stable and averages around 1 percent (the assumed long run excess cost

growth rate).

Over the ten-year budget window there is a lot of variation in inferred excess cost growth

between categories (see Table 3). Interestingly, there is not a consistent relationship between the

magnitude of excess cost growth and the choice of model. In most cases, excess cost growth is
19

largest when the age-sex-TUD model is used. Using the age-sex-TUD model, average excess

cost growth rates vary over the period 2003 to 2013 from a high of 5.2 percent for home health to

a low of -0.6 percent for skilled nursing facilities. The range when using the age-sex model is 

-0.7 percent for skilled nursing facilities to 4.9 percent for home health. 

These differences, although generally small, can affect long-run forecasts because not

only are the excess cost growth rates different, but so are the cost indices. As Figure 5 shows,

using the age-sex cost model results in the highest forecast of Medicare costs as a percentage of

GDP, and the time-until-death model results in the lowest forecast. The cost forecast using the

age, sex, and time-until-death model falls between the forecasts that use an age-and-sex index

and a time-until-death index separately. Interestingly, Miller (2001) believes that a model

including both age and time until death will understate future costs. Results here, however, show

that the model using only time until death produces lower cost forecasts than the model using

age, sex, and time until death.

Figures 6 through 12 show the category specific and Medicare managed-care forecasts as

a percentage of GDP. Note that through 2013 all forecasts are identical and match official CBO

forecasts (on a calendar-year basis). The cost index that produces the highest forecast varies by

category. For inpatient hospital (Figure 6), the age-sex index produces the highest forecast,

reaching 3.5 percent of GDP by 2078 (up from 1 percent in 2003); using an age-sex-TUD index

results in a slightly lower forecast, at 3.2 percent of GDP by 2078. 

As figure 8 shows, there is some variation in the forecast for SNF costs. Here using a

TUD index results in the lowest forecast and an age-sex index the highest. Using a TUD index,

SNFs are projected to rise to 0.40 percent of GDP by 2078 (from 0.13 percent in 2003); using an
20

age-sex index increases that forecast to 0.49 percent. For home health costs (Figure 9), using a

TUD index results in the lowest forecast, at 0.83 percent of GDP by 2078; using an age-sex-TUD

index results in the highest cost forecast at, 0.93 percent of GDP. For hospice (Figure 7), an agesex index results in the highest cost. Using the age-sex index, hospice costs are predicted to

reach 0.25 percent of GDP by 2078; using an age-sex-TUD or a TUD index, that forecast falls

slightly, to 0.22 percent.

Looking at outpatient hospital (Figure 10), costs are highest using an age-sex index and

lowest using a TUD index. Specifically, using the age-sex index, costs reach 0.83 percent of

GDP by 2078 (up from 0.12 percent in 2003), and using a TUD index, costs reach 0.78 percent

of GDP. Physician and other Part B costs (Figure 11) follow a different pattern. Using a TUD

index, costs are forecast to reach 2.8 percent of GDP by 2078 (up from 0.78 percent of GDP in

2003). An age-sex-TUD index results in the lowest forecast, at 2.73 percent of GDP. Managedcare costs (Figure 12) are predicted to be highest when using an age-sex index, reaching 0.99

percent of GDP by 2078. A TUD index results in the lowest forecast, at 0.90 percent of GDP. 

The category-specific results are summarized in Figure 13.

Finally, looking back at total costs (where “total” is defined as the sum of the parts, not a

separately forecast total), overall the age-sex forecast is the highest and the age-sex-TUD forecast

is the lowest (Figure 5). Total costs are predicted to reach 9.27 percent of GDP by 2078 using an

age-sex-TUD index and 9.75 percent using an age-sex index. 

Other researchers have found a difference of 14 percent in 2070 between an age and a

time-until-death model (Miller, 2001) and a difference of 16 percent in lifetime expenditures

between a simple model using age and sex and an expanded model that adds time until death for
21

66- to 70- year-olds (Stearns and Norton, 2002). The differences found here are smaller and

likely result from the fact that the model used here forecasts Medicare costs for all Medicare

beneficiaries rather than just for elderly beneficiaries or a subset thereof. 

Holding excess cost growth constant, by 2078 total medicare costs as a percentage of

GDP, projected using an age-sex model, are just over 5 percent higher than those projected using

an age-sex-TUD model. The differences between the highest and the lowest cost forecasts (in

terms of percentage of GDP) range from 14 percent for hospice to just over 1 percent for home

health costs in 2078. So, although the choice of model does affect cost forecasts over the long

term, the differences in the forecasts do not alter the conclusion that the financial burden on the

Medicare system will increase significantly.
22

References

Board of Trustees, Federal Hospital Insurance and Federal Supplementary Medical Insurance

Trust Funds, The 2003 Annual Report of the Federal Hospital Insurance and Federal

Supplementary Medical Insurance Trust Funds, Washington, D.C., U.S. Government

Printing Office, March 2003.

Congressional Budget Office. Uncertainty in Social Security's Long-Term Finances: A

Stochastic Analysis, Congressional Budget Office, Washington, D.C., December 2001.

Cutler, David M. and Louise Sheiner, “Demographics and Medical Care Spending: 

Standard and Non-Standard Effects,” NBER Working Paper 6866, National Bureau of

Economic Research, Cambridge, Mass., 1998.

Cutler, David M. and Ellen Meara, “The Concentration of Medical Spending: An Update,”

NBER Working Paper 7279, National Bureau of Economic Research, Cambridge, Mass.,

1999

Garber, Alan M., Thomas E. MaCurdy, and Mark C. McClellan, “Medical Care at the 

End of Life: Diseases, Treatment Patterns, and Costs,” NBER Working Paper 6748, 

National Bureau of Economic Research, Cambridge, Mass., 1998.

Lubitz, James, James Beebe, and Colin Baker, “Longevity and Medicare Expenses,” New 

England Journal of Medicine. 332(1995): 999-1003.

Lubitz, James, Liming Cai, Ellen Kramarow, and Harold Lentzner, “Health, Life Expectancy,

and Health Care Spending among the Elderly,” New England Journal of Medicine.

349(2003): 1048-1055.

Lubitz, James, D. and Gerald F. Riley, “Trends in Medicare Payments in the Last Year of 

Life,” New England Journal of Medicine. 328(1993): 1092-1096.

Miller, Tim, “Increasing Longevity and Medicare Expenditures,” Demography, vol. 38, 

no 2 (May 2001): 215-226.

Spilman, Brenda C. and James Lubitz, “The Effect of Longevity on Spending for Acute 

and Long-Term Care,” New England Journal of Medicine. 342(2000): 1409-1415.

Stearns, Sally C. and Edward C. Norton, “Time to Include Time to Death? The Future of 

Health Care Expenditure Predictions,” Triangle Health Economics Working Paper Series

no. 4 (2002).
23

Table 1

Summary Statistics

Model R-squared

Estimated $

(standard errors)

Mean of 

Dependent

Variable

$/Mean of

Dependent

Variable

(3) Age-Sex-TUD 0.15 118.0

 (1.1)

3,526.5 0.033

(1) Age-Sex 0.01 111.1

 (1.1)

3,526.5 0.031

(2) TUD 0.13 121.3

 (1.1)

3,526.5 0.034
24

Table 2

Summary Statistics, Category-Specific Indices

Model R-squared

Estimated $

(standard errors)

Mean of Dependent 

Variable

$/Mean of

Dependent Variable

Inpatient Hospital

 Age-Sex-TUD 0.12 22.0

(0.8)

1,818.7 0.012

 Age-Sex 0.01 17.6

(0.8)

1,818.7 0.009

 TUD 0.11 23.2

(0.8)

1,818.7 0.013

SNF

 Age-Sex-TUD 0.05 26.5

(0.1)

136.9 0.194

 Age-Sex 0.02 26.4

(0.1)

136.9 0.193

 TUD 0.04 26.8

(0.1)

136.9 0.196

Hospice

 Age-Sex-TUD 0.03 5.2

(0.1)

29.0 0.180

 Age-Sex 0.00 5.1

(0.1)

29.0 0.175

 TUD 0.02 5.2

(0.1)

29.0 0.178

Home Health

 Age-Sex-TUD 0.03 64.5

(0.3)

396.2 0.163

 Age-Sex 0.01 63.9

(0.3)

396.2 0.161

 TUD 0.02 65.0

(0.3)

396.2 0.164

Outpatient Hospital

 Age-Sex-TUD 0.11 -2.6

(0.2)

928.5 -0.003

 Age-Sex 0.01 -3.9

(0.2)

928.5 -0.004

 TUD 0.09 -2.8

(0.2)

928.5 -0.003

Physicians/Other Part B

 Age-Sex-TUD 0.03 19.5

(0.2)

335.6 0.058

 Age-Sex 0.01 18.8

(0.2)

335.6 0.056

 TUD 0.02 21.1

(0.2)

335.6 0.063
25

Table 3

Inferred Excess Cost Growth Detail by Category

Percent Change, 2003-2013

Medicare

Benefits

Cost Index

times

Population

GDP per

Capita

Derived

Excess Cost

Growth

Age-Sex-TUD

 Hospital Inpatient 6.3% 2.2% 4.1% 0.0%

 Skilled Nursing Facilities 5.6 2.5 4.1 -0.6

 Home Health 11.8 2.4 4.1 5.2

 Hospice 9.0 2.6 4.1 2.7

 Hospital Outpatient 10.9 2.4 4.1 4.2

 Physicians/Other Part B 7.5 1.7 4.1 0.8

 Group Plans 3.4 -2.9 4.1 2.4

Total 6.8 1.7 4.1 1.0

Age-Sex

 Hospital Inpatient 6.3% 2.3% 4.1% -0.2%

 Skilled Nursing Facilities 5.6 2.5 4.1 -0.7

 Home Health 11.8 2.6 4.1 4.9

 Hospice 9.0 2.3 4.1 2.3

 Hospital Outpatient 10.9 2.5 4.1 4.1

 Physicians/Other Part B 7.5 1.7 4.1 0.7

 Group Plans 3.4 -2.8 4.1 2.3

Total 6.8 1.7 4.1 0.8

TUD

 Hospital Inpatient 6.3% 2.5% 4.1% -0.1%

 Skilled Nursing Facilities 5.6 2.6 4.1 -0.8

 Home Health 11.8 2.5 4.1 5.0

 Hospice 9.0 2.5 4.1 2.7

 Hospital Outpatient 10.9 2.5 4.1 4.1

 Physicians/Other B 7.5 2.5 4.1 0.9

 Group Plans 3.4 -2.7 4.1 2.3

Total 6.8 1.9 4.1 0.9
Figure 1

Aggregate Age-Sex Indices

0.0

0.5

1.0

1.5

2.0

2.5

223242526266687072747678808284868890

Age

Index

Men

Women
Figure 2

Aggregate Costs Indices by Time Until Death, Men

0

2

4

6

8

10

12

14

223242526266687072747678808284868890

Age

Index

TUD=1

TUD=2

TUD=3
Figure 3 

Aggregate Costs Indices by Time Until Death, Women

0

5

10

15

20

25

223242526266687072747678808284868890

Age

Index

TUD=1

TUD=2

TUD=3
Figure 4

Excess Cost Growth Using an Age-Sex vs. an Age-Sex-TUD Index

-10

-5

0

5

10

15

20

1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012

Age-Sex Age-Sex-TUD
Figure 5

Total Medicare Costs as a Percentage of GDP

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073 2078

Percentage of GDP

tud age-sex-tud age-sex
Figure 6 

Inpatient Hospital Costs as a Percentage of GDP

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073 2078

Percentage of GDP

age-sex tud age-sex-tud
Figure 7 

Hospice Costs as a Percentage of GDP

0.00%

0.05%

0.10%

0.15%

0.20%

0.25%

0.30%

2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073 2078

Percentage of GDP

age-sex tud age-sex-tud
Figure 8 

SNF Costs as a Percentage of GDP

0.00%

0.05%

0.10%

0.15%

0.20%

0.25%

0.30%

0.35%

0.40%

0.45%

0.50%

2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073 2078

Percentage of GDP

age-sex tud age-sex-tud
Figure 9 

Home Health Costs as a Percentage of GDP

0.0%

0.1%

0.2%

0.3%

0.4%

0.5%

0.6%

0.7%

0.8%

0.9%

1.0%

2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073 2078

Percentage of GDP

tud age-sex-tud age-sex
Figure 10 

Outpatient Hospital Costs as a Percentage of GDP

0.0%

0.1%

0.2%

0.3%

0.4%

0.5%

0.6%

0.7%

0.8%

0.9%

2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073 2078

Percentage of GDP

tud age-sex-tud age-sex
Figure 11 

Physician and Other Part B Service Costs as a Percentage of GDP

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073 2078

Percentage of GDP

age-sex age-sex-tud tud
Figure 12 

Medicare Managed Care Costs as a Percentage of GDP

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

1.2%

2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073 2078

Percentage of GDP

tud age-sex-tud age-sex
Figure 13 

Category Specific Costs as a Percentage of GDP

Using an Age-Sex-TUD Index

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

Inpatient

Hospital

SNF Home Health Hospice Outpatient

Hospital

Physician

and Other

Part B

Group Plans

2003 2028 2053 2078