Persistence in Medicare Prescription Drug Expenditures by Treatment Class 

Jessica S. Banthin, G. Edward Miller 

September, 2006 

ABSTRACT

The chronic nature of many prescription drugs used by Medicare beneficiaries suggests 

that adverse selection could pose a problem in the market for stand-alone prescription 

drug plans that were created under the Medicare Modernization Act of 2003. Using 

panel data on the Medicare population from the Medical Expenditure Panel Survey, we 

find that prescription drug expenditures are less concentrated but more persistent over 

time than are total health expenditures. We also find that information on drug treatment 

categories increases the power of prediction models. Such information could be useful in 

developing improved risk adjustment models designed to minimize adverse selection. 

Jessica S. Banthin 

Director, Division of Modeling and Simulation 

Center for Financing, Access and Cost Trends 

Agency for Healthcare Research and Quality 

U.S. Department of Health and Human Services 

Agency for Healthcare Research and Quality (AHRQ) 

540 Gaither Road 

Rockville, MD 20850 

JBanthin@AHRQ.gov

G. Edward Miller 

Economist, Division of Modeling and Simulation 

Center for Financing, Access and Cost Trends 

Agency for Healthcare Research and Quality 

U.S. Department of Health and Human Services 

Agency for Healthcare Research and Quality (AHRQ) 

540 Gaither Road 

Rockville, MD 20850 

EMiller@AHRQ.gov

2
 

Introduction

The passage of the Medicare Modernization Act (MMA) in 2003 has focused 

attention on stand alone prescription drug insurance policies for Medicare beneficiaries. 

Prior to the MMA, stand alone prescription drug plans were not commonly available in 

the current health care system either to Medicare beneficiaries or others covered by 

private group and non-group insurance. This means there is little private market 

experience to guide the implementation of this new Medicare policy. It also raises the

question of why stand alone drug policies were not commonly available. One possible 

reason for the lack of such policies is the problem of adverse selection which can threaten 

the efficiency and stability of an unsubsidized private insurance market. The MMA 

legislation implicitly recognizes this possibility by requiring the development of risk 

adjustment methods to reimburse insurers and by mandating substantial subsidies to 

premiums in order to attract both low and high risk beneficiaries to the plans. Additional 

legislative provisions to minimize adverse selection include reinsurance and risk 

corridors that shield insurers from bearing the full financial risk of prescription drug 

plans in the early years of the program. Finally, there are provisions that penalize 

beneficiaries who do not enroll in a prescription drug plan (PDP) immediately upon 

gaining Medicare eligibility. 

Despite the various provisions written into the MMA, the chronic nature of many 

prescription drugs used by Medicare beneficiaries suggests that adverse selection could 

still pose a problem for the stability of the market over time. Adverse selection occurs 

when high risk persons, those with higher probabilities of having high expenditures, are 

3
 

more likely to purchase the insurance policies than are low risk persons. This can happen 

when individuals are better able to predict their future health expenditures than are 

insurers who therefore can’t adjust the premiums to reflect higher levels of risk. Adverse 

selection also occurs, however, when policies are required to be sold at community rated 

premiums which are not adjusted for individual variation in risk. The MMA requires that 

stand along drug plans be sold at community rated premiums. In this type of market, 

even a small degree of adverse selection on the part of beneficiaries may lead to premium

increases that eventually drive low risk beneficiaries out of the market. 

It is well known that many of the medical conditions for which Medicare 

beneficiaries use prescriptions drugs are chronic in nature, such as arthritis, high blood 

pressure, high cholesterol, diabetes, mental illness, ulcers, allergies and others (Boccuti, 

Moon, Dowling, 2003). Since prescription drugs for such chronic conditions are usually 

prescribed to be taken on a maintenance basis, this suggests that high levels of 

expenditures for prescription drugs may persist year after year among Medicare 

beneficiaries with certain chronic conditions. In fact, evidence shows prescription drug 

expenditures are easier to predict than expenditures for other types of health care services 

such as physician office visits and hospital stays (Pauly and Zeng, 2003; Wrobel et al 

2004). Even among chronically ill beneficiaries, the probability of continued prescription 

drug purchases may be easier to predict than the probability of visiting the physician or 

requiring a hospital stay. One implication of this finding is that Medicare beneficiaries

who anticipate persistently high drug expenditures may be more likely to enroll in a PDP 

than low risk beneficiaries when premiums are community rated. A second implication 

of the predictable nature of prescription drug expenditures is that insurers have an 

4
 

incentive to avoid high risk beneficiaries, unless risk adjusted payments are able to 

compensate for their higher costs. Thus, two potential threats to the efficiency and 

stability of the market for PDPs exist when adverse selection is present. 

 In this paper we make use of nationally representative data on the noninstitutionalized Medicare population to predict individuals’ future prescription drug 

expenditures. We use information on year one’s prescription drug use in order to predict 

year two’s prescription drug expenditures. The information on year one’s use of drugs is 

data that would be readily available to insurers from their claims databases. In our data 

each prescription drug purchase is associated with a therapeutic class and subclass. 

Therapeutic class and subclass indicate the main treatment purpose of the drug and thus 

reveal information on the health conditions of the drug user. For example, the purchase 

of an anti-cholesterol drug indicates that the person likely has high cholesterol. The 

therapeutic class and subclass information is similar to having medical condition data of 

the type that is coded on physician and hospital claims. In some instances, a drug 

purchase provides more important information than a medical diagnosis. For example, 

when diabetes is identified through a drug purchase the disease may be more serious than 

diabetes identified through a medical diagnosis since the physician coded diagnosis 

includes those who do and do not need insulin. As another example, hypertension can be 

treated several different types of drugs (diuretics, beta blockers, ACE inhibitors, etc.) 

which vary considerably in terms of their cost. Information on the drugs used to treat the 

condition, therefore, may provide more information than a medical diagnosis of 

hypertension. 

Our study evaluates the statistical power of drug therapeutic information to 

5
 

predict next year’s expenditures using the current year’s experience. Our models also 

control for a wide array of health status and socio-demographic characteristics. Two year 

panels from the 1996 through 2000 Medical Expenditure Panel Survey are pooled 

together. Unlike private prescription drug claims data sets, these data are representative 

of the entire non-institutionalized Medicare population. We pay particular attention to 

treatment categories that are associated with Medicare-Medicaid dual eligibles and other 

low-income and disabled Medicare beneficiaries. These groups are eligible for 

substantial subsidies under the MMA but may have risk factors that make them less 

attractive to insurers offering stand-alone prescription drug plans. 

Previous Literature

There is a large literature focused on predicting total health care costs (see

Moeller, JF, et al. 2003) and developing risk adjustment methods for the Medicare 

population (see Pope, GC, Ellis, RP, Ash, A, et al, 2000). Most of this research focuses 

on the use of inpatient and ambulatory claims based diagnostic condition coding to 

predict future health care costs. Some papers have examined the use of pharmacy based 

therapeutic condition codes but very few of these papers focus on the prediction of 

prescription drug costs (see Zhao, Y., Ellis RP, Ash AS, et al. 2001). In a recent paper, 

Wrobel, et al (2004) predict prescription drug costs for the Medicare population 

comparing several models including one based on the CMS’ diagnosis cost group/ 

hierarchical condition category (DCG/HCC) risk adjustment model. However, they do 

not have access to pharmacy based therapeutic condition codes. 

Pauly and Zheng (2003) examine the problem of adverse selection in the market 

6
 

for PDPs. Using claims data for the under 65 population, they find that prescription drug 

expenditures are more predictable than other types of health care expenditures. In any 

given year, prescription drug expenditures show a similar degree of concentration as 

other types of health care expenditures, but over time persons who were in the top 

quintile of prescription drug expenditures are more likely to remain in the top quintile 

compared to persons who were in the top quintile of expenditures in other categories. 

According to Pauly and Zheng, adverse selection is more likely when there is a greater 

degree of persistence of high expenditures over time for two related reasons. First, 

persistence is high so it is easy to predict next year’s expenditures. Second, risk averse 

people who are willing to pay above expected costs in order to reduce their risk of an 

unanticipated high cost event are not willing to pay as much of a risk premium to avoid 

the high but predictable expenditures associated with chronic prescription drug 

expenditures. Pauly and Zheng extrapolate their findings to the Medicare population and 

estimate that the premium for a stand alone drug policy would have to be subsidized in 

the range of 71 to 91 percent of the total cost in order for 80 percent of Medicare 

beneficiaries to purchase it. 

Methods 

In this paper, we use population based survey data on Medicare beneficiaries that 

follows individuals for a period of two years. Specifically, to predict an individual’s 

“year two” prescription drug expenditures we use information from the individual’s “year 

one” prescription drug utilization. These models are estimated using ordinary least 

squares (OLS) regressions with probability weights and corrections for the MEPS 

7
 

complex sample design and can be characterized by the following equation: 

 RxExp2 = α + β1TC1 + β2TSC1 + β3Demo1 + β4RxExp1 + ε

The dependent variable (RxExp2) is total drug expenditures in year two expressed in 

2000 U.S. dollars. The key independent variables are sets of dummy variables that 

indicate whether each individual filled one or more prescriptions in each of 13 

therapeutic classes (TC1) or 59 subclasses (TSC1) in year one. The more detailed set of 

dummy variables categorizes drugs in over 200 therapeutic subclasses, but to avoid 

overfitting the models we omit subclasses that contain fewer than 15 sampled persons in 

any of the subpopulations that we examine. Since therapeutic subclasses are nested 

within therapeutic classes we never include both types of variables in a single model, but 

we compare the predictive power of these two sets of variables across model 

specifications. We also examine the explanatory power of our therapeutic class and 

subclass variables in combination with an extensive set of year one socioeconomic and 

health status variables (Demo1) and with year one total drug expenditures (RxExp1). 

We are interested in testing the predictive power of information drawn from the 

therapeutic categories of prescription drugs. Prescription drug therapeutic classes and 

subclasses are of interest in risk adjustment models for two major reasons. One, the data 

could be easily drawn from pharmacy based claims records and could be readily available 

to insurers in a timely manner. Two, in some cases, the identification of a chronic 

condition by the use of certain drugs used to treat the condition provides more 

information than the identification of a chronic condition by medical records. Chronic 

8
 

conditions are likely to be more severe when beneficiaries are treating them with 

prescription drugs compared to those conditions identified by medical records alone. 

Therefore, prescription drug utilization data may also be useful for refining existing risk 

adjustment methods. The main drawback of relying on therapeutic information based on 

prescription drug use is that off-label use of drugs may not be properly accounted for and 

some drugs have a variety of labeled uses. In our data set therapeutic information is 

based on the most common reasons for prescribing the drug. Some drugs, however, are 

more likely to be used for off-label purposes than others. 

For comparative purposes, we contrast the predictive power of therapeutic 

categories based on prescription drug purchases with models that include medical 

condition information, collected from household respondents. We construct two medical 

condition variables that are at similar levels of aggregation as the drug therapeutic class 

and subclass variables. Aggregate medical conditions (AgCond) consists of 12 dummy 

variables while detailed medical conditions (DeCond) consists of 50 dummy variables 

that indicate whether an individual has reported one of the aggregate or detailed medical 

conditions. In some variations of our model, we substitute the medical conditions 

variables for the therapeutic class variables to compare their explanatory power in 

predicting prescription drug expenditures. The models are characterized as follows: 

 

RxExp2 = α + β1AgCond1 + β2DeCond1 + β3Demo1 + β4RxExp1 + ε

Since detailed conditions are nested within aggregate conditions we never include both 

types of variables in a single model. 

9
 

Finally, for comparison purposes, we predict total non-drug and total health 

expenditures using either drug therapeutic subclasses or detailed medical conditions. If 

drug therapeutic categories prove to be significant predictors of prescription drug 

expenditures, then they may also be convenient and significant predictors of other types 

of health expenditures. 

To compare various models in terms of their predictive power, we use linear 

regression models where the dependent variable is measured in dollars. This functional 

form is widely used in the risk adjustment literature (Pope, GC, RP Ellis, AS Ash, et al., 

2000) because of its ease of interpretation. Also, the R-square is a standard measure of 

the fit of the model and, in this case, provides a measure of the degree to which second 

year expenditures can be predicted by the independent variables. To more accurately 

compare the measures of R-Square across different specifications and subsamples we 

construct standard errors for each R-square using balanced repeated replicates (BRR). 

We use 128 random samples which are drawn accounting for the strata and primary 

sampling units (PSUs) used in the MEPS. Next, regressions are run and the resulting Rsquare is computed 128 times using these randomly drawn subsamples which are 

approximately half the size of the original sample. Finally, standard errors are computed 

using the variation of these 128 estimates of R-square around the estimate of R-square 

from the original model. 

After demonstrating the predictive power of prescription drug therapeutic 

categories we develop scores to assess the relative risk of specific groups of Medicare 

beneficiaries. We predict prescription drug expenditures for every individual in our 

10
 

sample, using a regression model that includes therapeutic subclass indicators, 

sociodemographic and health status measures. Then for every individual we compute the 

ratio of the predicted expenditures to the mean. We report relative risk scores, expressed 

as percentages of the mean, for policy relevant groups. Thus, the relative risk scores 

identify those groups who are predicted to have higher or lower relative costs. Groups 

with higher predicted costs may be vulnerable to risk selection on the part of insurers if 

government reimbursement methods do not adequately account for their high risks. 

Particular attention is warranted for the more vulnerable Medicare populations including 

the disabled and those who are currently eligible for both Medicaid and Medicare. 

Data

The data for this study are based on two year panel files from the Medical 

Expenditure Panel Survey (MEPS). The MEPS is a household survey representative of 

the U.S. civilian, non-institutionalized population and collects data on health utilization

and expenditures, sources of payment, insurance coverage, employment, income, and 

individual and family sociodemographic characteristics including various measures of 

health status and medical conditions. The MEPS is designed as overlapping two year 

panels. Households are interviewed five times over a two year period of study and a new 

panel is begun each year. For this study, we combine data from the first four panels of 

the MEPS: Panel 1 was followed for 1996 and 1997, Panel 2 was followed for 1997 and 

1998, and so on through Panel 4 which was followed for 1999 and 2000. We pool all 

four panels together in order to increase our sample size and improve the ability to 

predict expenditures for smaller subgroups. We rename all variables so that they refer to 

11
 

year 1 or year 2 of their respective panels. To predict total prescription drug expenditures 

in year 2 we use information from year 1. 

The MEPS contains detailed information on each drug purchased by sampled 

individuals including the total price paid for the drug, the medication name and the 

National Drug Code (NDC). We assign a therapeutic class and subclass to each drug 

purchased by using the NDC to link the MEPS prescribed medicines files to the Multum

Lexicon database, a product of Cerner Multum, Inc. The Multum therapeutic 

classification system categorizes drugs in a manner which is designed to replicate the 

type of organizational schemes used in practice by physicians and pharmacists.1

 

For purposes of this paper, we include persons of any age with 12 months of 

Medicare coverage in year two of their panel. Thus, we include persons who were just 

starting their Medicare period of eligibility. We exclude persons who died or were 

institutionalized during the second year of the panel. By pooling four panels together our 

sample is larger and we can examine smaller subpopulations of policy interest such as the 

disabled Medicare beneficiaries under age 65 as well as those with Medicaid coverage. 

By pooling several years of data, however, we are ignoring changes over time in the 

patterns of prescription drug utilization. For example, between 1997 and 2001 the 

number of Medicare beneficiaries who purchased cholesterol lowering drugs increased 

from 4.9 to 10.5 million. During the same time period, the number of Medicare 

beneficiaries who purchased COX-2 inhibitors, a new subclass of analgesics, increased 

from 0 to 5.5 million (Moeller, et. al. 2005). Changes in the patterns of drug prescribing 

will continue to occur as new drugs are developed and introduced so the changes that 

 

1

 Approximately 4.5 percent of drug purchases linked to more than one therapeutic class and were assigned 

to a unique class using a combination of condition information and random assignment. About 1.0 percent 

of drug purchases could not be linked to Multum. 

12
 

take place during the time period of our study do not diminish the policy relevance of the 

results. 

In some specifications we use medical conditions as reported by household 

respondents to compare the predictive power of drug therapeutic categories against 

medical condition categories at similar levels of aggregation. Medical conditions in the 

MEPS are collected at various points during the interview. Respondents are asked if 

individuals in the household have any medical conditions at the beginning of each round 

of the survey. Additional medical conditions may be recorded whenever there is a 

medical event such as a visit to the doctor or hospital to explain the reason for the visit. 

In order to minimize some of the differences between years of data, we adjust 

prescription drug expenditures using the consumer price index (CPI) for prescription 

drugs. By using this inflation adjuster we are capturing both general price inflation as 

well as prescription drug price inflation that reflects the higher prices of newly introduced 

drugs. For the purposes of this analysis, this adjustment factor is the most appropriate. 

Our purpose is to try to make the four years of data as similar as possible. At the same

time, it should be noted that we include panel year dummy variables in all of our 

regressions to control for panel effects. 

Results

In the following results, Tables 1 and 2 show how prescription drug expenditures 

differ from total and non-drug expenditures in terms of their concentration and 

persistence over time. These two aspects of the expenditure distribution have important 

implications for the design of efficient health insurance markets. Next, in Tables 3 and 4, 

13
 

we present selected health status and socioeconomic characteristics of the Medicare 

population and subpopulations examined in this study. We also contrast the use of 

prescription drugs by common therapeutic classes and subclasses by subgroups of the 

Medicare population. In Tables 5 and 6, we present our regression models and compare 

their predictive power across various specifications and subgroups. Finally, in Table 7, 

we present relative risk ratios that show how certain groups of Medicare beneficiaries are 

much more expensive on average than others in terms of their predicted drug 

expenditures. 

 Prescription drug expenditures are not distributed evenly across the Medicare 

population and a large portion of aggregate expenditures is accounted for by a small 

percent of the population. In the top panel of Table 1, beneficiaries are ranked according 

to their prescription drug expenditures. We see that the top one percent accounts for 8.8 

percent of all prescription drug expenditures and the top 10 percent of beneficiaries 

accounts for 40.3 percent. The median drug expenditure in our sample is $564 which is 

less than one-tenth the amount of the threshold estimate of $5,961 that identifies those in 

the top one percent of the distribution. 

Prescription drug expenditures, however, are slightly less concentrated than nondrug and total expenditures. In the second panel of Table 1, we see that the top one 

percent of persons account for 17.3 percent of non-drug health expenditures while the top 

10 percent of persons account for 62.1 percent of non-drug expenditures. The third panel 

of Table 1 shows that the top one percent of persons accounts for 14.7 percent of total

health care expenditures while the top ten percent of persons accounts for 54.8 percent of 

total health expenditures. Non-drug and total health expenditure are more skewed than 

14
 

drug expenditures because of rare, high cost events like extended hospitalizations. 

The more concentrated or skewed the expenditure distribution the greater the 

potential for differences between low risk and high risk beneficiaries. A highly skewed 

distribution also means that a few high risk individuals can have a disproportionate 

impact on a plan’s total costs. If risk adjusted payments don’t fully account for some

predictable expenditures by high health risk enrollees, then plans will have an incentive 

to discourage enrollment of certain high risk patients. Reinsurance provisions, however, 

can mitigate this potential problem.

Along with less concentration, prescription drug expenditures are also more 

persistent over time compared to non-drug and total health expenditures. In the top panel 

of Table 2 we see that among beneficiaries whose prescription drug expenditures placed 

them in the top five percent in year 1, 50.5 percent of them remained in the top five 

percent in year 2. In the bottom panel, however, we see that among beneficiaries whose 

total expenditures placed them in the top five percent in year 1, only 24.4 percent of them 

remained in the top five percent in year 2. Thus beneficiaries are more than twice as 

likely to have persistently high prescription drug expenditures as persistently high total 

health expenditures over a two year period. The more persistent are expenditures from 

one year to the next, the easier it is to predict which individuals will be high risk 

individuals for the plan. Adverse selection is more likely when there is greater degree of 

persistence of high expenditures over time. 

Table 3 presents selected socioeconomic and health status characteristics for the 

total Medicare population as well as for policy relevant subgroups. These characteristics 

are based on “year one” information from the four pooled panels of MEPS data. In the 

15
 

first column we see that our sample of beneficiaries (excluding those who died and were 

institutionalized) represents 34.3 million persons per year on average. About 13.4 

percent of the population live below the poverty line while 25.0 percent live between 100 

and 200 percent of poverty. About 39.0 percent of the population have employer 

sponsored private supplemental insurance coverage, while 18.2 percent hold private nongroup coverage including Medigap plans, and 14.7 percent are covered by Medicaid or 

some other public insurance in addition to their Medicare.2

 More than one-third of 

beneficiaries report having fair or poor health. 

In the second panel from the left of Table 3 we compare the characteristics of 

Medicare beneficiaries who are age 65 and older to the characteristics of those under 65. 

The population under age 65 qualifies for Medicare because of permanent disabilities.3

 

The disabled Medicare population represents a potentially vulnerable subgroup that may 

have different prescription drug utilization patterns than the much larger aged population. 

Disabled Medicare beneficiaries are more likely to be Black and Hispanic than are aged 

beneficiaries. Nearly one-third (32.5 percent) of disabled beneficiaries live in poverty 

compared to 10.8 percent of the aged. More than twice as many disabled beneficiaries 

report having fair or poor health compared to aged beneficiaries (68.6 vs. 30.4 percent). 

Disabled beneficiaries are also more likely than the aged to have trouble with ADLs and 

IADLs (34.7 vs 13.6 percent). It is not surprising that 75.3 percent of the disabled versus 

23.8 percent of the aged report activity limitations including limitation in the ability to 

 

2

 The MEPS does not measure which Medicare beneficiaries hold Medicare HMO coverage. Beneficiaries 

without private or public supplemental coverage include those with FFS Medicare as well as those with

Medicare HMOs. 

3

 Disabled Medicare beneficiaries are classified as aged beneficiaries upon turning age 65. MEPS cannot 

identify persons who formerly held Medicare as a disabled beneficiary. 

16
 

work, do housework, or attend school. 

In the third panel across of Table 3 we compare Medicare subgroups by insurance 

status. Although insurance status is likely to change as the MMA takes full effect in 

2006, persons who are currently enrolled in both Medicaid and Medicare also represent a 

potentially vulnerable subgroup. Their prescription drug coverage will become part of 

the total Medicare benefits they receive, rather than being covered by state Medicaid 

programs. More than one-third of the dually enrolled live in poverty (35.7 percent) 

compared to 5.9 percent of those with private group insurance. About 54.7 percent of the 

dually enrolled report fair or poor health status compared to 27.7 percent of those with 

private group insurance. Dually enrolled Medicare beneficiaries are also much more 

likely than those with private group insurance to have trouble with ADLs and IADLs, 

activity limitations, and difficult walking. 

Table 4 shows how utilization of some common prescription drug therapeutic 

subclasses varies across subgroups of Medicare beneficiaries. Most of the selected 

therapeutic subclasses in the table ranked in the top 10 in purchases or expenditures 

among the Medicare population during the period 1997 to 2001. A couple of additional 

subclasses are included because of large and significant coefficients in our prediction

models. Cardiovascular drugs are among the most commonly used drugs within the 

Medicare population. Among the full Medicare population shown in the first column, we 

find that on average during this four year period, 20.7 percent of the total community 

Medicare population purchased at least one prescription per year for calcium channel 

blockers, 19.5 percent a diuretic, 16.0 percent ACE inhibitors, and 15.4 percent a beta 

blocker. In addition to these anti-hypertensive medications, about 14.6 percent of all 

17
 

Medicare beneficiaries purchased an anti-cholesterol drug. Several hormone drugs, antidepressants, and analgesics were also purchased by more than 10 percent of the full 

Medicare population. 

When we compare the aged and disabled Medicare populations in the second 

section of Table 4, however, differences emerge between the two groups. For instance 

the aged are more likely to use a cardiovascular drug than are the disabled. On the other 

hand, the disabled Medicare population is much more likely to use psychotherapeutic 

drugs than is the aged population. About 23.6 percent of the disabled purchased at least 

once prescription per year for an antidepressant during this four year time period 

compared to only 9.1 percent of the aged. Use of anti-psychotics was more than 5 times 

greater in the disabled population compared to the aged (10.5 vs 1.9 percent). Many of 

the disabled qualify for Medicare coverage because of severe mental illness. The 

disabled are also greater users of some analgesics and bronchiodilators. 

There is a large degree of overlap between the disabled Medicare population and 

the dually enrolled Medicare and Medicaid population, so some of the patterns repeat 

themselves when we compare the dually enrolled to beneficiaries with private 

supplemental insurance, in the third section of Table 3. Use of anti-depressants, antipsychotics, some analgesics and H2 antagonists is greater among the dually enrolled than 

among privately insured beneficiaries. Also, Medicare beneficiaries enrolled in Medicaid 

are more likely to use bronchiodilators and less likely to use sex hormones and thyroid 

drugs than are Medicare beneficiaries with private coverage. The use of cardiovascular 

drugs shows mixed patterns. Anti-cholesterol drugs and beta blockers are more common 

among those with private group insurance compared to those who are enrolled in 

18
 

Medicaid and Medicare. 

In Table 5, we present measures of R-square from several different regression 

models predicting prescription drug expenditures in year two based on information from 

year one. The models differ by the inclusion of different sets of independent variables. 

In the first column of Table 5, we predict expenditures for the total Medicare population. 

In the first row model 1 shows that using therapeutic class information by itself allows us 

to predict 25.9 percent of the variation in year two drug expenditures. Recall that 

therapeutic class information consists of dummy variables indicating whether there was 

any use within a set of 13 broadly defined classes of drugs. The R2

 for model 2 increases 

to 36.8 percent when we use the more detailed therapeutic subclass dummy variables 

which refer to 59 more narrowly defined drug categories. The aggregate and detailed 

prescription drug therapeutic categories are statistically significantly more powerful in 

predicting future drug expenditures than are the corresponding self reported medical 

conditions. For example, model 2 which contains therapeutic subclasses explains 36.8 

percent of the variation compared to model 4 which contains self-reported medical 

conditions model and explains 26.5 percent of the variation (p < .05). 

The comparison of drug therapeutic categories versus medical condition 

categories is slightly tilted in favor of the therapeutic categories since, by definition, 

information on therapeutic category is also an indicator of use of medical services. The 

drug therapeutic information doesn’t exist unless a beneficiary has purchased a 

prescription. In many cases, purchasing a prescription often implies a prior medical visit 

as well. Household reported medical conditions, on the other hand, are not always 

associated with a concurrent medical event. Nonetheless, medical conditions provide a 

19
 

useful point of reference in assessing the gain in prediction resulting from drug 

therapeutic information. 

Model 5 which includes demographic and some health status measures has 

relatively little explanatory power (R2

 = 13.8). Using prior year drug expenditures as in 

model 6, on the other hand, increases the measure of R2

 to 42.8 percent. Yet, the 

standard error of the R2

 for model 6 is 7.6%, higher than for any of the other models. 

Since there is a lot of variation in prior year drug expenditures and this is the only 

variable in the model except for panel dummy variables, it is not surprising that the 

standard error is relatively high. 

In the lower rows of Table 5 we combine sets of explanatory variables. Model 7 

which combines demographic and drug therapeutic subclass variables explains more 

variance in year 2 expenditures (R2 = 39.4) compared to model 8 which combines 

demographic and detailed medical condition variables (R2 = 30.6).4

 The strongest 

specification is model 9, containing therapeutic subclass dummy variables, 

sociodemographic and health status measures, and year one’s total prescription drug 

spending, which explains 52.6 percent of year two’s drug expenditures. 

These patterns are similar across all of the different subgroups whose 

expenditures we model. Although it is inappropriate to directly compare measures of R2

across groups, we can compare the rankings of the models across groups.5

 In other 

words, for all subgroups the demographic variables explained the least amount of 

variation. Similarly, the drug therapeutic subclass model dominated the detailed medical 

 

4

 Significant at the 10 percent level (p < .10) 5

 Differences in samples sizes of the subgroups can affect the measure of R2. With small samples, there is 

also a danger of overfitting the regression model. To avoid overfitting we dropped therapeutic subclasses 

that contained fewer than 15 persons. 

20
 

conditions model, although the differences in R2 for these two competing models were 

not statistically significant except for the full population and the 65 and over population. 

Across all subgroups except the privately insured, the model containing therapeutic 

subclass dummy variables, sociodemographic and health status measures plus year one’s 

total expenditures on drugs explained more than half of the variation in year two drug 

expenditures. 

Table 6 shows how much easier it is to predict drug expenditures than non-drug or 

total health expenditures in the Medicare population. Measures of R2 are consistently 

much lower in the non-drug and total health expenditure models than in the drug 

expenditure models, reflecting the greater levels of persistence in drug expenditures from

one year to the next. We also see that drug therapeutic subclass information works at 

least as well as detailed medical condition information in predicting non-drug and total 

health expenditures (differences in R2 are not statistically significant). 

In Table 7, we use the regression model 7 from Table 5 containing therapeutic 

subclass dummy variables plus sociodemographic and health status measures estimated 

on the entire Medicare population to generate relative risk scores. We compute the ratio 

of predicted year two expenditures divided by mean year two expenditures. Expressed as 

a percent, these relative risk scores indicate which groups of Medicare beneficiaries are 

predicted to have above or below average expenditures. Thus, the scores indicate which 

groups represent high or low risks to an insurance company selling stand alone PDPs. 

The scores also indicate which groups have the greatest demand for prescription drugs 

and are more likely to enroll in the voluntary plans when other alternatives are lacking or 

less generous. 

21
 

We chose to measure predicted expenditures as a function of mean expenditures 

because the mean serves as a policy relevant benchmark against which to measure risk. 

If the PDPs were not subsidized by the government and community rating were mandated 

then premiums would be a function of the mean expense plus some loading factor. The 

highly skewed distribution of prescription drug expenditures, however, implies that the 

mean is greater than the median. In our data about 33.6 percent of the population spent 

more than the mean expense of $962, while the median person in the Medicare 

community population spent about $564. Expressed as a function of the mean, the 

median person would have a relative risk score of about 58.6 percent.6

 Thus, groups with 

an average relative risk score greater than 100 percent are high risk groups. 

Table 7 shows that male and female beneficiaries under age 65 have a higher than 

average risk of using prescription drugs. Men under age 65 are predicted to spend 124.8 

percent of mean expenditures while women under age 65 are predicted to spend 179.1 

percent of the mean. Blacks and Hispanics are predicted to spend less relative to the 

mean compared to whites and others (93.6 and 91.4 vs 101.2 percent). These predictions 

hold insurance status constant using population means so that the relative risk scores 

would not reflect differences in current insurance status. It is interesting that poverty 

status by itself does not show much variation in relative risks. This may reflect the fact 

that not all Medicare beneficiaries who are eligible for Medicaid coverage are enrolled. 

Thus persons living in poverty include those with relatively generous Medicaid coverage 

as well as those without any supplemental coverage. On average they are not especially 

 

6

 The full regression model used to predict the relative risk ratios is included in Appendix One. For the 

prediction of relative risk ratios we held the insurance parameters constant and used population averages. 

This eliminates variation in relative risk resulting from current insurance status which will likely change as 

the Medicare legislation is implemented in 2006. 

22
 

high relative risk. 

As expected health status signals large variations in relative risk. Persons 

reporting they are in fair or poor health have a relative risk score of 146.7 percent of the 

mean while those in very good or excellent health have a relative risk score of 57.5 

percent. Similarly, persons reporting ADLs or IADLs have a relative risk of 149.8 

percent while persons with activity limitations and difficulty walking have relative risks 

of 153.7 percent and 138.8 percent respectively. 

Of particular interest, however, are the relative risks of persons who have used at 

least one prescription in the indicated therapeutic subclasses. Self-reported health status, 

disability status, and poverty status may not be easily measured by insurers but use of 

drugs by therapeutic categories will be readily available after one year’s experience. 

Such information may also be available from firms’ experiences with the Medicare 

discount drug program. As shown on the right hand side of Table 7, use of drugs in any 

of the listed categories raises the relative risk to more than 100 percent. Individuals 

prescribed anti-anginal agents or anti-psychotics are predicted to spend more than twice 

the mean expenditure, putting them into the very top of the distribution of drug 

expenditures. Individuals using proton pump inhibitors and anti-depressants are 

predicted to spend 197.9 and 196.3 percent of the mean, respectively. The use of ACE 

inhibitors, anti-cholesterol drugs, anti-diabetic agents, H2 antagonists, and 

bronchiodilators also raise relative risk scores to very high levels (173.6, 176.5, 186.7, 

174.7, and 173.4 percents respectively). When the relative risk score is greater than 100 

percent, it means not only that people in the group have high prescription drug 

expenditures in the current year but that they are also predicted to have high drug 

23
 

expenditures next year, too. 

Discussion

This study demonstrates how the MEPS data can support longitudinal analyses of 

the entire Medicare population including those with and without current prescription drug 

coverage. In addition, the inclusion of the National Drug Code allows us to link to a 

secondary data base containing information on prescription drug therapeutic classes and 

subclasses.7

 The MEPS small sample sizes remain a limitation, however, in terms of

refining risk adjustment methodologies. The results of this study should be tested on 

larger Medicare datasets based on claims data. 

In this paper we have shown that prescription drug expenditures are highly 

persistent over a two year period. About half of the variation in prescription drug 

expenditures can be predicted using information from the previous year, including 

therapeutic categories based on drug utilization. The high degree of persistence in 

prescription drug expenditures over time contrasts with the level of persistence in nondrug and total health expenditures, and implies that adverse selection is potentially a 

greater problem in markets for stand-alone prescription drug plans than in other health 

insurance markets. 

In order to minimize the potential for adverse selection, the MMA requires the 

development of risk adjustment methodologies for PDPs. In this paper we also show that 

information on drug therapeutic categories can significantly increase the predictive power 

of regression models. Drug therapeutic categories are based on pharmacy claims and 

 

7

 The NDC is not available in the Medicare Current Beneficiary Survey, the other large household survey

database. 

24
 

would be readily available to insurers making them a useful tool for risk adjustment 

methodologies. 

This study also shows that users of certain drugs are more likely to have high 

predicted prescription drug costs compared to others. The implications of this 

predictability are two-fold. One, without adequate risk adjusted payments Medicare PDP 

insurers may have an incentive to discourage such groups from enrolling in their plans. 

Two, individuals in these groups may be more likely to enroll in Medicare PDPs if they 

do not have access to alternatives. In the short term insurers will not be put at much 

financial risk for their performance so adverse selection is not a major concern. It bears 

careful watching, however, since some of the most vulnerable Medicare beneficiaries use 

certain prescription drugs for the treatment of their chronic conditions that will mark 

them as high risk individuals. 

25
 

References

Boccuti, C, M. Moon, K Dowling. Chronic Conditions and Disabilities: Trends and

Issues for Private Drug Plans. The Commonwealth Fund. Policy Brief 680. October 

2003. http://www.cmwf.org/programs/medfutur/boccuti_chronicconditions_pb_680.pdf accessed May 5, 

2004. 

Moeller, JF, Cohen SB, Mathiowetz, NA, Wun L. Regression-based Sampling for 

Persons with High Health Expenditures: Evaluating Accuracy and Yield with the 1997 

MEPS. Medical Care. 2003, 41(7) Supplement, ppIII-44-III52. 

Moeller JF, Miller GE, Banthin JS. Looking Inside the Nation’s Medicine Cabinet: 

Trends in Outpatient Drug Spending by Medicare Beneficiaries, 1997 and 2001. Health 

Affairs 23(5): 217-225. 

Monheit, AC. Persistence in Health Expenditures in the Short Run: Prevalence and 

Consequences. Medical Care. 2003, 41(7) Supplement, ppIII-53-III-64. 

Pauly, MV and Y. Zeng. Adverse Selection and the Challenges to Stand-Alone 

Prescription Drug Insurance. Working Paper 9919. National Bureau of Economic 

Reseach. August 2003. http:/www.nber.org/papers/w9919 

Pope, GC, RP Ellis, AS Ash, et al. Principal Diagnostic Cost Group Model for Medicare 

Risk Adjustment. Health Care Financing Review, Vol 21, No.3. Spring 2000, pp 93-118 

Wrobel, MV, J. Doshi, B.Stuart, B.Briesacher. Predictability of Prescription Drug 

Expenditures for Medicare Beneficiaries. Health Care Financing Review, Vol 25, No.2. 

Winter 2003-2004, pp 37-46 

26
Table 1 

Distributions of Expenditures (in 2000 Dollars) by Service: 

Non-Institutionalized Medicare Beneficiaries, 1996-2000 

Percent of Medicare Population 

Ranked by Drug Expenditures 

Expenditure

Threshold 

Percent of 

Total Dollars 

Prescription Drug Expenditures 

 Top 1 Percent $5,961 8.8% 

 Top 5 Percent 3,269 25.9 

 Top 10 Percent 2,412 40.3 

 Top 25 Percent 1,295 67.5 

 Top 50 Percent 564 90.3 

 Mean Expenditure (st. error) 962 (22) 

Non-Drug Expenditures 

 Top 1 Percent $53,453 17.3% 

 Top 5 Percent 21,683 45.3 

 Top 10 Percent 11,749 62.1 

 Top 25 Percent 3,887 84.0 

 Top 50 Percent 1,127 95.7 

 Mean Expenditure (st. error) 4683 (165) 

Total Expenditures 

 Top 1 Percent $55,060 14.7% 

 Top 5 Percent 23,858 39.4 

 Top 10 Percent 13,445 54.8 

 Top 25 Percent 5,369 77.2 

 Top 50 Percent 2,144 92.3 

 Mean Expenditure (st. error) 5645 (171) 

Source: Authors' calculations from pooled MEPS data, 1996 

through 2000.
Table 2 

Persistence of Health Expenditures by Service: 

Non-Institutionalized Medicare Beneficiaries, 1996-2000 

Year One Year Two Expenditure Category 

Expend. 

Cat. Top 5 Top 10 Top 25 Top 50 

Prescription Drug Expenditures 

percent distribution 

Top 5 50.5 72.8 89.8 97.3 

Top 10 34.5 54.5 83.8 96.2 

Top 25 17.7 33.6 66.8 91.9 

Top 50 9.9 19.6 45.8 80.6 

Non-Drug Expenditures 

percent distribution 

Top 5 23.4 38.4 61.5 81.9 

Top 10 18.0 31.7 57.2 81.8 

Top 25 11.7 21.8 46.6 74.0 

Top 50 7.7 14.7 35.7 66.4 

Total Expenditures 

percent distribution 

Top 5 24.4% 40.4 69.1 88.8 

Top 10 18.2 31.4 60.9 86.8 

Top 25 12.1 22.8 49.4 79.6 

Top 50 8.2 15.8 38.5 71.7 

Source: Authors' calculations from pooled MEPS data, 1996 

through 2000.
Table 3 

Socioeconomic and Health Status: Non-Institutionalized Medicare Beneficiaries, 1996-2000 

 Age Supplementary Insurance 

 

Full 

Population 

65 and 

Older 

Less 

than 65 

Private 

Group 

Private 

NonGroup Public 

Total Population 

(millions)1 34.3 30.3 4.0 13.4 6.2 5.1 

 Percent of Beneficiaries 

Age 

19 to 64 11.8% 0.0% 100.0% 8.1%** 2.9%** 29.1%** 

65 to 74 50.2 57.0 0.0 59.4** 51.7 36.8** 

75 and Older 38.0 43.0 0.0 32.5** 45.3** 34.0** 

Gender 

Male 43.5 42.2 53.3* 48.8** 37.6** 38.4** 

Race / Ethnicity 

Black 9.1 7.8 18.1* 6.5** 2.9** 19.0** 

Hispanic 5.6 5.2 8.4* 2.8** 2.5** 15.3** 

White and Other 85.4 86.9 73.5* 90.8** 94.6** 65.7** 

Income as a Percentage 

of the Poverty Line 

Less than 100% 13.4 10.8 32.5* 5.9** 9.2** 35.7** 

100 to 200% 25.0 24.6 27.9 17.6** 28.1** 33.1** 

More than 200% 61.6 64.6 39.6* 76.5** 62.7 31.2** 

Supplementary 

Insurance Status 

Private Group 39.0 40.6 26.6* 100.0 0.0 0.0 

Private Non-Group 18.2 20.0 4.5* 0.0 100.0 0.0 

Public 14.7 11.9 36.4* 0.0 0.0 100.0 

No Supplement 28.1 27.6 32.4* 0.0 0.0 0.0 

Marital Status 

Married 54.5 55.9 43.6* 69.8** 54.0 27.1** 

Not Married 45.5 44.1 56.4* 30.2** 46.0 72.9** 

Education 

Less than High School 34.5 34.0 38.3 24.6** 31.1** 55.6** 

High School Grad 34.6 34.0 39.0* 36.9** 36.0 27.5** 

Some College, or more 30.9 31.9 22.6* 38.5** 32.8 16.9** 

Health Status 

Poor or Fair 34.9 30.4 68.6* 27.7** 30.5** 54.7** 

Good 33.1 34.9 19.5* 34.7 32.4 27.1** 

Very Good or Excellent 32.0 34.7 11.9* 37.6** 37.1** 18.2** 

Disability Status 

IADL or ADL 16.1 13.6 34.7* 10.7** 12.8** 33.4** 

Activity Limitation 29.9 23.8 75.3* 21.0** 23.5** 55.0** 

Difficulty Walking 40.1 37.6 58.8* 33.9** 38.4 53.7** 

Source: Authors' calculations from pooled MEPS data, 1996 through 2000

Note: 1. Average annual population from 1996-2000.

*The difference in estimates for the 65+ and < 65 populations is significant at the p < .05 level. 

**The estimate for an insurance category is different from the full population at the p < .05 level. 
Table 4 

Percentage of Population Using Selected Therapeutic Subclasses of Drugs: 

by Age and Supplementary Insurance Status: 

Non-Institutionalized Medciare Beneficiaries, 1996-2000 

 Age 

Supplementary

Insurance 

Therapeutic Subclass 

Full

Population 

65 and

Older 

Less 

than 65 

Private 

Group 

Private 

NonGroup Public

Total Population (millions)1 34.3 30.3 4.0 13.4 6.2 5.1

 percent of beneficiaries with at least one purchase 

Cardiovascular Drugs 

Calcium Channel Blockers 20.7% 21.3% 16.2%* 20.9% 19.1% 23.5%

ACE Inhibitors 16.0 16.5 12.6* 16.6 16.0 15.6 

Diuretics 19.5 20.4 12.4* 18.5 21.8 20.7

Beta Blockers 15.4 15.8 11.6* 16.7** 15.2 11.8** 

Antihypertensive Comb. 9.7 10.3 5.4* 9.1 11.4 9.0 

Anti-Cholesterol (Statins) 14.6 15.0 11.4* 17.1** 15.6 11.0**

Anti-Anginal Agents 7.0 7.1 6.4 7.2 5.5 9.0**

Hormones 

Sex Hormones 14.1 13.9 15.1 17.2** 13.9 10.8** 

Antidiabetic Agents 13.1 13.0 14.6 13.0 11.4 15.6**

Thyroid Drugs 10.4 10.8 7.0* 11.1 10.9 7.6** 

Antineoplastics 3.6 3.6 2.9 3.7 4.3 3.3

Gastrointestinal Drugs 

H2 Antagonists 8.4 8.1 10.8* 8.7 7.5 12.1** 

Proton Pump Inhibitors 6.7 6.4 9.0* 7.1 5.5 7.4 

Psychotherapeutic Drugs 

Anti-Depressants 10.8 9.1 23.6* 9.9 10.2 15.8*

Anti-Psychotics 2.9 1.9 10.5* 2.1** 2.0** 7.0**

Analgesics 

NSAIDs 14.7 13.5 23.0* 15.3 11.6** 18.9**

COX-2 Inhibitors 1.6 1.6 1.9 1.6 1.5 2.8**

Respiratory Tract Drugs 

Bronchiodilators 6.5 5.9 10.4* 6.1 5.2 10.2**

Upper Respiratory Comb. 9.7 9.5 11.4 10.1 11.0 8.9 

Source: Authors' calculations from pooled MEPS data, 1996-2000

Note: 

1. Average annual population from 1996-2000. 

*The difference in estimates for the 65+ and < 65 populations is significant at the p < .05 level. 

**The estimate for a given insurance category is different from the estimate for the full population 

at the p < .05 level. 
Table 5 

Percentage of Year Two Drug Expenditures Explained by Year One Variables: 

Measures of R-Square by Model, Age and Supplementary Insurance Status: 

Non-Institutionalized Medicare Beneficiaries, 1996-2000 

Age Supplementary Insurance 

Variables Included 

in Model 

Full 

Population 

65 and 

Older 

Less 

than 65 

Private 

Group 

Private 

NonGroup Public 

1. Therapeutic Class (TC) 25.9 30.2 24.8 24.0 27.7 27.3 

(2.4) (1.2) (5.7) (3.8) (4.8) (6.1) 

2. Therapeutic Subclass (TSC) 36.8 42.1 40.4 36.5 46.2 39.9 

 (3.3) (2.2) (10.1) (6.3) (7.1) (9.4) 

3. Aggregate Conditions 

(ACond) 18.3 22.3 17.3 17.9 23.1 17.1 

(1.7) (1.1) (4.3) (2.5) (3.4) (5.2) 

4. Detailed Conditions (DCond) 26.5 32.7 24.3 28.9 32.8 25.2 

 (2.7) (2.0) (9.3) (4.7) (7.7) (7.4) 

5. Demographic (Dem) 13.8 16.0 12.8 15.1 22.2 16.1 

(1.6) (1.2) (8.4) (3.3) (5.9) (5.1) 

6. RX Expenditure (Year 1) 42.8 49.5 33.9 33.5 46.3 54.4 

 (7.6) (2.0) (16.6) (13.9) (4.0) (4.7) 

7. TSC and Dem 39.4 44.7 44.8 40.0 51.7 44.8 

(3.5) (2.4) (12.6) (7.2) (8.6) (10.3) 

8. DCond and Dem 30.6 36.1 31.8 33.0 40.8 33.9 

 (3.0) (2.2) (13.2) (5.8) (9.3) (9.2) 

9. TSC, Dem, and Year1 52.6 56.4 57.3 49.3 61.5 64.8 

(4.3) (2.2) (12.7) (9.2) (6.9) (7.1) 

10. DCond, Dem and Year1 50.5 55.3 51.8 46.4 57.8 63.6 

 (5.0) (2.1) (14.6) (9.7) (7.0) (6.4) 

Sample Size 6826 5914 912 2412 1187 1305 

Source: Authors' calculations from pooled MEPS data, 1996-2000

Note: Standard errors in parentheses estimated with balanced repeated replicates using 128 

replicates 
Table 6 

Percentage of Year Two Health Expenditures Explained by Year One Variables: 

Measures of R-Square by Model and Type of Health Expenditure: 

Non-Institutionalized Medicare Beneficiaries, 1996-2000 

Variables Included 

in Model 

Drug 

Expenditures 

Non-Drug 

Expenditures Total 

Expenditures 

(standard errors)1 

1. Therapeutic Subclass 

(TSC) 36.8 9.1 12.1 

(3.3) (2.1) (2.0) 

2. Detailed Conditions 

(DCond) 26.5 7.4 9.7 

 (2.7) (1.8) (1.9) 

3. TSC and Dem 39.4 12.5 15.5 

(3.5) (2.8) (2.9) 

4. DCond and Dem 30.6 11.5 14.1 

 (3.0) (2.6) (2.5) 

5. TSC, Dem, and Year12 52.6 15.4 18.6 

(4.3) (3.5) (3.3) 

6. DCond, Dem and Year12 50.5 15.0 18.1 

 (5.0) (3.3) (3.1) 

Sample Size 6826 6826 6826 

Source: Authors' calculations from pooled MEPS data, 1996-2000

Notes: 

1. Standard errors (in parentheses) are estimated using 128 balanced repeated replicates 

2. Year one expenditures are defined as drug expenditures for the drug expenditure 

model. Year one expenditures are defined as two separate variables (drug and non-drug 

expenditures) for the non-drug expenditure and total expenditure models. 
Table 7 

Predicted Drug Expenditures as a Percentage of Mean Expense:1 

by Selected Sub-Groups of the 

Non-Institutionalized Medicare Population, 1997 to 2000 

Demographic Characteristics Therapeutic Subclass 

Age / Gender Cardiovascular Drugs 

Less than 65, male 124.8%* Calcium Channel Blockers 166.6%**

Less than 65, female 179.1** ACE Inhibitors 173.6** 

65-74, male 86.5** Diuretics 163.3** 

65-74, female 93.9* Beta Blockers 149.5** 

75 and older, male 90.8** Antihypertensive Comb. 133.5** 

75 and older, female 100.8 Anti-Cholesterol (Statins) 176.5** 

Race / Ethnicity Anti-Anginal Agents 205.3** 

Black 93.6 Hormones 

Hispanic 91.4 Sex Hormones 136.1** 

White and Other 101.2 Antidiabetic Agents 186.7** 

Income as a Percentage of 

the Poverty Line Thyroid Drugs 140.2** 

Less than 100% 107.6 Antineoplastics 151.1** 

100 to 200% 102.7 Gastrointestinal Drugs 

More than 200% 97.2 H2 Antagonists 174.7** 

Health Status Proton Pump Inhibitors 197.9** 

Poor or Fair 146.7** Psychotherapeutic Drugs 

Good 91.8** Anti-Depressants 196.3** 

Very Good or Excellent 57.5** Anti-Psychotics 206.1** 

Disability Status Analgesics 

ADL or IADL 149.8** NSAIDs 132.1** 

Activity Limitations 153.7** COX-2 Inhibitors 159.7** 

Difficulty Walking 138.8** Respiratory Tract Drugs 

Total Prior Drug Use Bronchiodilators 173.4** 

No Drug Purchases in Year1 10.1** Upper Respiratory Comb. 109.9** 

Source: Authors' calculations from pooled MEPS data, 1997-2000

Notes: 

* p < .10, ** p < .05 that estimate is different from 100%. 

1. Expenditures are predicted with a model that includes therapeutic subclass and 

demographic variables. Coefficients for this model are given in Appendix One. 
Appendix One 

Regression Coefficients: Association of Year One Therapeutic Subclass and 

Demographic Variables with Year Two Drug Expenditures 

Therapeutic Subclass 

Variables Coefficient

Therapeutic Subclass 

Variables Coefficient

Antimalarial Agents -$161 Antirheumatics 319* 

Cephalosporins -44 Antigout Agents 96 

Macrolides 11 Fibric Acid Derivatives 374* 

Miscellaneous Antibiotics -126 Nasal Preparations 217 

Penicillins 65 Antidepressants 446* 

Quinolones -52 Antipsychotics 725* 

Sulfonamides 170 Proton Pump Inhibitor 456* 

Tetracyclines -43 COX-2 Inhibitors 161 

Antineoplastics 363* Demographic Variables 

ACE Inhibitors 363* Less than 65, male 266 

Antidadrenergic Agents 

(Peri.) 340* Less than 65, female 492* 

Antidadrenergic Agents 

(Cent.) 295* 65-74, male 73* 

Antianginal Agents 254* 65-74, female 53 

Beta Blockers 205* Black -173* 

Calcium Channel Blockers 399* Hispanic -178* 

Diuretics 131* High School Grad 25 

Inotropic Agents -50 Some College 16 

Antihypertensive Comb. 245* College Degree 58 

Miscellaneous Analgesics 153 Advanced Degree 219* 

Narcotic Analgesics 100 Married 116 

NSAIDs 67 Widowed 154 

Salicylates -84 Divorced/Seperated 72 

Anticonvulsants 156* Employed -8.6 

Antiemetic/Antivertigo -18 Private Group Insurance 25 

Antiparkinson 146 Public Insurance 74 

Anxiolytics/Sedatives/Hyp. 100 Private Non-Group Ins. -15 

Muscle Relaxants 126 Income 100-199% Pov. Ln. 25 

Anticoagulants 277* Income 200-399% Pov. Ln. 54.9 

Antiplatelet Agents 331* Income 400% or more -28.1 

Anticholinergics/Antispas. -171 Good Health 60 

GI Stimulants 161 Fair Health 141* 

H2 Antagonists 273* Poor Health 216* 

Laxatives -201 Good Mental Health -19 

Adrenal Cortical Steroids 450* Fair Mental Health -98 

Antidiabetic Agents 497* Poor Mental Health 18 

Sex Hormones 157* IADL -53 

Thyroid Drugs 148* ADL 87 

Genitourinary Tract Agents 656* Activity Limitation 162* 

Iron Products 390* Difficulty Walking 69 

Mineral and Electrolytes 271* Rural Northeast 107 

Vitamins 194 MSA Northeast 75 
Appendix One: Continuued 

Regression Coefficients: Association of Year One Therapeutic Subclass and 

Demographic Variables with Year Two Drug Expenditures 

Therapeutic Subclass Variables Coefficient 

Therapeutic 

Subclass 

Variables Coefficient 

Vitamins and Mineral Comb. 59 Rural Midwest 100 

Antihistamines 70 MSA Midwest 162* 

Bronchiodilators 279* Rural South 62 

Respiratory Inhalants 30 MSA South 28 

Upper Respiratory Comb. -151* MSA West -34 

Dermatological Agents -21 Panel1 -59 

Opthalmic Preparations 148* Panel2 -40 

Anticholesterol (Statins) 495* Panel 3 76 

Narcotic Analgesic Comb. -13 

Number of Obs. 6826 R-Square 0.394 

Source: Authors' calculations from pooled MEPS data, 1997-2000

* indicates p < .05