Estimated Effects of Increased Coverage on Prescription Drug Expenditures among Seniors Merrile Sing and Edward Miller and Jessica S. Banthin

Agency for Healthcare Research and Quality Working Paper No. 08005 March 2008

Suggested citation: Sing M, Miller E, Banthin JS. Estimated Effects of Increased Coverage on

Prescription Drug Expenditures among Seniors. Agency for Healthcare Research and Quality

Working Paper No. 08005, March 2008, http://gold.ahrq.gov.

AHRQ Working Papers provide preliminary analysis of substantive, technical, and methodological

issues. The papers have not undergone external peer review. They are distributed to share

valuable experience and research. Comments are welcome and should be directed to the

authors. The views expressed are those of the authors and no official endorsement by the

Agency for Healthcare Research and Quality or the Department of Health and Human Services is

intended or should be inferred.
 

 

 

 

Estimated Effects of Increased Coverage on Prescription Drug Expenditures among Seniors 

March 2008 

ABSTRACT 

Access to prescription drugs is important to seniors with chronic conditions. In 2005, 

approximately one-third of seniors lacked prescription drug coverage. To examine the potential 

effects of gaining drug coverage, which many seniors achieved in 2006 with Medicare Part D, 

we estimate the relationship of prescription drug coverage and drug expenditures. We use 

econometric models and data from a nationally representative sample of elderly, noninstitutionalized Medicare beneficiaries pooled for the years 2000 – 2003. Relative to no drug 

coverage, coverage from Medicare HMOs or employer-based plans is associated with increased 

prescription drug expenditures of approximately 15 and 20 percent respectively, and with a 

marginal increase of approximately one unique drug purchased per year. Medicaid and 

employer-based coverage are associated with greater purchases of newer analgesic agents. 

Findings from this paper can be used to inform Part D benefit design and formularies. 

Merrile Sing, Ph.D. 

Agency for Healthcare Research and Quality 

Division of Modeling and Simulation 

Center for Financing, Access, and Cost Trends 

540 Gaither Road 

Rockville, MD 20850 

(301) 427-1666 (phone) 

merrile.sing@ahrq.hhs.gov 

G. Edward Miller, Ph.D. 

Agency for Healthcare Research and Quality 

Division of Modeling and Simulation 

Center for Financing, Access, and Cost Trends 

540 Gaither Road 

Rockville, MD 20850 

(301) 427-1681 (phone) 

ed.miller@ahrq.hhs.gov 

Jessica Banthin, Ph.D. 

Agency for Healthcare Research and Quality 

Division of Modeling and Simulation 

Center for Financing, Access, and Cost Trends 

540 Gaither Road 

Rockville, MD 20850 

(301) 427-1678 (phone) 

jessica.banthin@ahrq.hhs.gov 

2
 
 

Estimated Effects of Increased Coverage on Prescription Drug Expenditures among 

Seniors 

INTRODUCTION 

Prescribed medications play a critical role in treating and controlling many chronic 

conditions. Access to prescription drugs is therefore very important to the vast majority of 

seniors who have one or more chronic medical conditions. Until Medicare Part D was 

implemented in 2006, Medicare did not cover outpatient prescription drugs, and approximately 

one-third of non-institutionalized seniors lacked prescription drug coverage in 2005 (Neuman et 

al. 2007). Medicare Part D is expected to have an important positive impact on seniors’ access 

to prescription drugs, but until individual level expenditure data are available for study in late 

2008 or early 2009, we will not know its impact on the typical senior.1 We use data from before 

the enactment of Medicare Part D to estimate how expanded insurance coverage for drugs will 

increase utilization and expenditures for individual enrollees, for all drugs and for specific 

therapeutic classes of drugs. 

Neuman et al. (2007) describes the characteristics of Part D enrollees and their first year 

experiences, based on a national (but not nationally representative) survey of seniors. As 

expected, the study finds that enrollees reported increased access to drugs. For instance, 

compared with seniors without drug coverage in 2006, seniors with Part D reported that they 

were less likely to spend $300 or more per month out-of-pocket on prescription drugs, after 

controlling for socioeconomic characteristics, medication use, and health status. However, 

Neuman et al (2007) could not collect information on total expenditures validated by insurance 

claims or pharmacy reports, as is done in expenditure surveys like the Medicare Current 

Beneficiary Survey (MCBS) and Medical Expenditure Panel Survey (MEPS). 

3
 
 

There are several other studies that examine the association of prescription drug coverage 

and drug use or spending in the Medicare population. However, most of these studies rely on 

older data and do not differentiate between types of insurance coverage. Further, all of the 

previous studies examine only aggregate drug use or expenditures. Using 1990 data that is not 

nationally representative, Lillard et al. (1999) estimated that adding prescription drug benefits to 

Medicare would increase the probability of any drug use by 8.8 percent for seniors without drug 

coverage, and 4.8 percent for all seniors. Other studies used nationally representative data from 

the MEPS or the Medicare Current Beneficiary Survey (MCBS) and found that expanded drug 

coverage would result in statistically significant increases in aggregate prescription drug 

expenditures or use. Curtis et. al. (2004) used data from the 1997 MEPS to estimate crosssectional models with a single binary drug coverage variable, and found that prescription drug 

insurance would increase expenditures of those without drug insurance by about 40 percent. 

Two recent papers use MCBS data and attempt to correct for the endogeneity of 

insurance status. Khan et. al. (2007) examine the effect of 4 types of drug coverage (HMO, 

Medigap, employer-sponsored, or public) estimating models first without, then with, individual 

fixed effects. Depending on the type of coverage, they estimated that drug use would increase by 

15 to 47 percent before controlling for fixed effects, and 6 to 14 percent after controlling for 

fixed effects. They conclude that cross-sectional models are subject to substantial endogeneity 

bias which is corrected by fixed effects. Shea et. al. (2007) estimate models with a single binary 

drug coverage variable first using a cross-sectional specification, and then using a residual 

inclusion model to correct for potential endogeneity. The magnitudes of the estimates from the 

alternative specifications are nearly equivalent with each model showing that prescription drug 

coverage would increase drug use among Medicare beneficiaries by almost 50 percent. 2 Based 

4
 
 

on these results, Shea et. al. conclude that selection into prescription drug coverage is predictable 

based on observable health. 

Other studies examine the association of prescription drug coverage with use of drugs 

from one specific therapeutic class, such as antihypertensive drugs (Adams et al, 2001; Blustein, 

2000) or cardiovascular drugs (Federman et al. 2001). 

NEW CONTRIBUTION 

This study estimates the association between four levels of prescription drug coverage 

and prescription drug use and expenditures. We improve upon previous studies by using more 

recent data, and we expand upon previous studies of aggregate drug use or spending by 

analyzing use of a wide range of therapeutic classes of drugs, focusing on the classes that 

account for the highest level of expenditures and use among seniors.3

 We estimate expenditures 

for 19 therapeutic drug classes and estimate which specific therapeutic classes of drugs will have 

higher expenditures in association with greater prescription drug coverage. 

By including specific therapeutic classes, we are able to examine potential reasons for 

changes in aggregate expenditures. Unlike previous studies, we can estimate the extent to which 

increased drug coverage is associated with (1) purchasing more refills of drugs that seniors 

already purchase, (2) replacing previously purchased drugs for specific medical condition(s) with 

newer drugs that treat the same medical condition(s), or (3) purchasing drugs to treat a medical 

condition that was not previously treated with prescription drugs. 

5
 
 

 

CONCEPTUAL MODEL 

To examine the relationship between drug coverage and prescription drug expenditures, 

we use a conceptual model of the demand for health care, which is derived from the demand for 

health. The factors affecting patients’ demand for health care include actual or perceived health 

status, sociodemographic characteristics, and economic factors (Feldstein 1979; Phelps 1992). 

Health status measures include self-rated health status and the presence of particular acute and 

chronic medical conditions. Greater use of health care is associated with lower levels of selfrated health and increases in the number and severity of medical conditions. Sociodemographic 

characteristics include age, gender, education, race/ethnicity, and geographic location. Age is 

positively associated with health care use. Higher levels of education may be reflected in greater 

knowledge about preventive care and treatment of specific medical conditions, different attitudes 

towards seeking care, and greater efficiency in purchasing care (Phelps, 1992). Race and 

ethnicity (Institute of Medicine, 2002) and geographic location (Welch et. al.1993) have been 

shown to be associated with different levels of treatment for specific conditions. Economic 

factors include income, prices and health insurance. People with higher levels of income tend to 

purchase more health care, all other factors equal. Health insurance lowers the price that patients 

pay for health care, and consumers purchase greater quantities of goods and services as their 

prices decline. 

The models we estimate can be characterized by the following equation: 

(1) RXist = f (health status it, sociodemographic characteristics it, income it, 

 insurance categoryit, attitudesit). 

6
 
 

 

Our measure of health care, (RXist), is annual drug use or expenditures by person i, for drug 

category s, in year t. Our primary model uses total expenditures for all types of outpatient 

prescription drugs. Next, we examine the number of unique drugs purchased. Then, we extend 

the analysis to separately examine 19 therapeutic classes and subclasses of drugs with high levels 

of expenditures by seniors. In our drug expenditure data, a non-trivial percent of persons have 

zero expenditures and the distribution of strictly positive expenditures is highly skewed. We 

accommodate these data issues by estimating two part models where the conditional expenditure 

model is estimated using Poisson models with log links (Buntin and Zaslavski, 2004; Manning 

and Mullahy, 2001). We estimate models of total purchases and the number of unique drugs 

using one part Poisson models that accommodate the distribution of these count data. 

Our models control for health status, sociodemographic characteristics, income, insurance 

category, and patient attitudes about their health care. We include variables capturing patient 

attitudes about health care (described in more detail below) as a partial control for the potential 

endogeneity of insurance status and prescription drug expenditures. 

DATA AND METHODS 

Data 

This study uses data from the Medical Expenditure Panel Survey (MEPS), an on-going 

survey that collects nationally representative data on health care use, expenditures, insurance 

coverage, health status, and socio-demographic characteristics for the U.S. civilian, noninstitutionalized population. MEPS expenditure data combine household-reported information 

primarily associated with medical care events with information obtained from providers through 

7
 
 

follow-back surveys to supplement household-reported data and to obtain more detailed data 

(Cohen, 2003). 

In addition, MEPS collects detailed information on all prescription medication purchases 

reported by respondents, including the medication name and the condition for which the drug 

was prescribed. The prescription data collected from households are enhanced through surveys 

of pharmacies where the drugs were purchased. For every reported purchase of a prescription 

drug, pharmacies are asked to provide data on the medication name, national drug code (NDC), 

strength, quantity, total charge, and payment by source. In this study, each drug purchased was 

assigned a therapeutic class and subclass using the NDC to link the MEPS prescribed medicines 

files to the Multum Lexicon database, a product of Cerner Multum, Inc. 

To increase sample sizes and statistical power, we pool data from MEPS for 4 years— 

2000 through 2003—and compute average annual estimates based on the pooled data. The study 

population includes people in the civilian, non-institutionalized population age 65 and older who 

were enrolled in Medicare during their first of 5 survey rounds in MEPS. Our total sample 

includes 7,036 individuals, representing an average of 33.4 million elderly Medicare 

beneficiaries per year between 2000 and 2003. 

Measures 

We examine three measures of total annual drug use and expenditures. Our primary 

model uses total expenditures for all types of outpatient prescription drugs. Next, we examine 

two measures of use: total purchases (e.g., number of prescriptions including refills) and the 

number of unique drugs purchased. In addition, we separately examine 19 therapeutic classes 

and subclasses of drugs with high levels of expenditures by seniors, including 

antihyperlipidemics, antidiabetic agents, proton pump inhibitors, antidepressants, analgesics, and 

8
 
 

others. Drug expenditures for all years are adjusted to 2006 dollars using the Consumer Price 

Index (CPI) for Prescription Drugs and Medical Supplies. 

The main variables of interest are binary variables that indicate 6 mutually exclusive drug 

coverage categories, hierarchically defined as: 

1.	 Medicaid 

2.	 Employer-based plan with prescription drug coverage (including TRICARE plans) 

3.	 Medicare HMO 

4.	 Individually-purchased Medigap plans with drug coverage 

5.	 Prescription drug assistance from the Veterans’ Administration (VA) or a State 

Pharmacy Assistance Program 

6. No prescription drug coverage, which serves as the reference category. 

Our insurance measures are based on the type of coverage beneficiaries reported during their first 

round in the survey, a “point-in-time” measure.4 We distinguish beneficiaries in employer-based 

or Medigap plans that do not cover prescription drugs and assign them to the ‘no drug coverage’ 

category along with beneficiaries who have no supplemental insurance coverage. 

The sociodemographic control variables include binary variables for age (3 categories), 

race/ethnicity, gender, education, region, year, death or entry into an institution, and health status 

reported by a household member. We control for 4 income categories relative to the federal 

poverty line: poor or near poor, low income, middle income, and high income, which are < 124 

percent, 125 – 199 percent, 200 – 399 percent, and 400 or more percent of the federal poverty 

line, respectively. The health status measures include overall self-ratings of physical and mental 

health, as well as whether the beneficiary reported having limited physical functioning, limited 

cognition, difficulties in activities of daily living (ADL), or difficulties in instrumental activities 

9
 
 

of daily living (IADL). Activities of daily living are activities related to personal care and 

include dressing and eating. Instrumental activities of daily living are activities related to 

independent living and include preparing meals and managing money (National Center for 

Health Statistics, 2007). 

We also include two variables that measure beneficiary attitudes toward risk and health 

care that control for different propensities to purchase insurance and use medical care. These 

variables indicate whether each beneficiary agreed “strongly” or “somewhat” with the following 

statements: “I am more likely to take risks than the average person” and “I can overcome illness 

without help from a medically trained person.” 

Finally, we include a set of binary variables that indicate whether beneficiaries reported 

that a doctor had ever diagnosed them with diabetes, asthma, high blood pressure, heart disease, 

stroke, emphysema, or arthritis. Because of the potential for endogenous reporting of conditions 

(i.e., beneficiaries who use drugs are more likely to have received a diagnosis) we estimate 

models with and without these conditions variables. 

Estimation Methods 

Given the skewed distribution of drug expenditure data, along with a non-trivial percent 

of persons with zero expenditure, we estimate two-part econometric models. The first part is a 

logistic regression that estimates the probability of any drug use and the second part estimates 

the level of expenditures given use. To account for the characteristics of the conditional 

expenditure distribution, we estimate Poisson models with log links (Buntin and Zaslavski, 2004; 

Manning and Mullahy, 2001).5

 Total predicted expenditures are calculated by combining results 

from both parts of the two-part Poisson model. Models of the number of unique drugs are 

estimated using Poisson regressions to properly account for the distribution of these count data. 

10
 
 

To estimate the marginal effects of drug coverage we use the method of recycled 

predictions. This method uses results from our models to calculate total predicted use and 

expenditures for the uninsured when they are uncovered and when they are simulated to have 

each of the 4 types of insurance. The marginal effect for each type of insurance is the difference 

between these estimates averaged across all uninsured persons. Standard errors for marginal 

effects are estimated using the method of balanced repeated replicates (BRR) (Wolter, 1985). 

BRR provides non-parametric estimates of standard errors that properly account for the complex 

survey design of MEPS. BRR is particularly useful in situations, such as the use of recycled 

predictions, where it is difficult, or impossible, to derive a closed-form solution of the Taylor 

Series standard error. In addition, BRR standard errors are known to be second order equivalent 

to bootstrap standard errors. 

A second estimation issue is the potential endogeneity of insurance status since 

unobserved beneficiary characteristics may affect both a beneficiary’s choice of insurance and 

their subsequent expenditures on prescription drugs. This may result in biased estimates of the 

impact of insurance on expenditures. However, previous studies found limited evidence of 

adverse selection in the Medicare supplemental market due to observable health status (Ettner, 

1997; Wolfe and Goddeeris, 1991) and found that supplemental coverage appealed to both low 

and high health risk beneficiaries (Marquis, 1992). These findings mitigate concerns about 

endogeneity for Medigap and Medicare HMO plans. More recent evidence regarding 

endogeneity is mixed. Khan et. al. (2007) present evidence of selection bias in cross-sectional 

models while Shea et. al. (2007) conclude that selection into prescription drug coverage is 

predictable based on observable health. 6 Our models control for health status and other 

observable characteristics using a set of variables that are similar to those used in Shea et. al 

11
 
 

 

(2007). In addition, we include variables that are typically unavailable in most data and that 

capture beneficiaries’ attitudes towards risk-taking behavior and the necessity of consulting a 

medical provider to overcome illness. 

RESULTS 

Baseline Expenditures by Insurance Category 

To provide a context for our estimates of how drug expenditures would potentially change 

when beneficiaries gain drug coverage, we show the distribution of insurance coverage among 

seniors and their associated spending patterns. As indicated in Table 1, we find that at any point 

in time between 2000 and 2003, an average of 12.9 million non-institutionalized elderly 

Medicare beneficiaries (or 38.6 percent) had no prescription drug insurance coverage. Among 

those with coverage, about 4.1 million beneficiaries were enrolled in a Medicare HMO and 1.5 

million had a Medigap plan with a drug benefit. Taken together, this indicates an additional 5.6 

million beneficiaries (or 16.5 percent) had limited drug coverage. 

Beneficiaries with Medicaid and employer-based drug coverage had higher average drug 

expenditures ($2,174 and $1,712, respectively) than those without drug coverage ($1,456) and 

also had higher median and 95th percentile expenditures. In contrast, there were no statistically 

significant differences in mean or 95th percentile expenditures between beneficiaries covered by 

Medicare HMOs or Medigap plans and those without coverage. However, beneficiaries with 

Medigap coverage had higher median expenditures ($1,321) than those without drug coverage 

($865). 

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Characteristics by Insurance Group 

Differences in expenditures by insurance category may result from differential selection 

of beneficiaries into insurance categories as well as differences in the generosity of benefits. 

Table 2 describes the characteristics of our sample and presents means of the control variables by 

insurance group. Beneficiaries in Medicaid and employer-based plans are different from 

beneficiaries without drug coverage in virtually every aspect of self-rated health and for many of 

the chronic conditions. In general, beneficiaries covered by Medicaid are less healthy while 

beneficiaries with employer-based coverage are healthier than those without coverage. In 

contrast, there are fewer differences in reported health status between beneficiaries covered by 

Medicare HMOs or Medigap plans and persons who did not have drug coverage. This supports 

previous research that there is little selection into those plans based on observable health (Ettner, 

1997; Wolfe and Goddeeris, 1991). In addition to health status, Table 2 shows a number of 

differences in other socio-economic characteristics between covered groups and those with no 

drug coverage. 

Generosity of Coverage 

Differences in expenditures by type of coverage are also affected by the generosity of 

benefits. Individuals with more generous coverage may be inclined to spend more than they 

would if benefits were less generous. The first row in Table 3 compares the average percent paid 

out of pocket across the existing categories of insurance coverage. Beneficiaries with Medicaid 

and employer-based plans have the most generous drug coverage and financed a relatively low 

percentage of their drug expenditures with out-of-pocket payments (25 percent and 33 percent, 

respectively). In contrast, beneficiaries with drug coverage from Medicare HMOs or Medigap 

13
 
 

plans paid a higher percentage out-of-pocket (54 percent and 53 percent respectively), and those 

with no coverage had the highest percentage (88 percent). Out-of-pocket costs for seniors with 

no coverage are less than 100 percent because some seniors may have obtained drug coverage 

after our point-in-time measure of insurance coverage, or because some seniors received 

payment for prescription drugs from sources (e.g., automobile insurance) that we do not classify 

as prescription drug coverage. Similarly, out of pocket costs for seniors with Medicaid are 

higher than might be expected because some lack full year coverage or because they purchase 

some products that Medicaid does not cover. 

The second row of Table 3 shows our estimates of what beneficiaries enrolled in each 

insurance category would pay out-of-pocket if they were instead enrolled in a Medicare Part D 

plan. For each insurance category, these estimates were produced by assuming that each 

beneficiary instead had prescription drug coverage from a standard Part D plan in 2006. In 2006, 

the standard Part D benefit package included a $250 deductible, 25 percent coinsurance until 

$2,250 in total drug expenditures, no coverage between $2,251 and $5,100, and 5 percent 

coinsurance after $5,100 in total prescription drug expenditures. We estimate that beneficiaries 

enrolled in each of our insurance categories would pay approximately half of their prescription 

drug expenditures out-of-pocket with Part D coverage. 

Estimated Effects of Insurance on Total Expenditures and Utilization 

The estimated coefficients for our two-part Poisson model of prescription drug 

expenditures (without the 7 chronic condition variables) are presented in Table 4. The results for 

the probability of use (part one) regression indicate that use of prescription drugs is positively 

associated with prescription drug coverage in employer-sponsored, Medigap, and Medicare 

14
 
 

 

HMO plans, poor/fair general health, limited physical functioning, and older age. In addition, 

we find that males, seniors in the Midwest, and those who believe they can over come illness 

without a medical professional are less likely to use prescription drugs. There is no statistically 

significant association between prescription drug use and education, income or race/ethnicity. 

Among seniors who use prescription drugs, we find that many of the same variables are 

also associated with greater prescription drug expenditures (Table 4). Prescription drug coverage 

from Medicaid or employer-based plans, poor/fair general health, limited physical functioning, 

difficulties with IADLs, and older age are associated with greater prescription drug expenditures. 

Males, seniors living in the West Census region, and those who believe they can over come 

illness without a medical professional spend less than those in their respective reference groups. 

Conditional upon prescription drug use, we find that greater drug expenditures are associated 

with enrollment in health plans with the most generous drug coverage—Medicaid and employersponsored plans. In contrast, our part one regression results indicate that Medicaid enrollment is 

not associated with an increased probability of prescription drug use.

 Table 5 displays our predicted marginal increases in expenditures (first two rows) and 

number of unique drugs purchased (last two rows) computed using the estimated coefficients 

from our multivariate models. For our model without chronic condition variables, Medicaid 

($480), employer-based ($288) and Medicare HMO ($217) coverage are all associated with 

statistically significant increases in prescription drug expenditures. Relative to the mean 

expenditure for beneficiaries with no drug coverage ($1,453), these estimates represent 

expenditure increases of 33, 20, and 15 percent, respectively. Inclusion of our 7 chronic 

condition variables result in smaller predicted increases in drug expenditures for all insurance 

15
 
 

categories, as shown in the second row of Table 5. For instance, the alternate model predicts 

increases of $392 for Medicaid and $195 for employer-based plans relative to no drug coverage. 

Our model without the 7 chronic conditions predicts that prescription drug coverage from 

Medicaid, employer-based plans, or Medicare HMOs is associated with a marginal increase of 

approximately one unique drug (unique active ingredient or combination) purchased during the 

year in addition to an increase in the number of refills of each drug, as shown in the third row of 

Table 5. On average, beneficiaries with no supplemental coverage purchased 4.7 unique drugs 

per year. Our simulation shows that when these beneficiaries gain prescription drug coverage 

from Medicaid, employer-based, or Medicare HMO plans they will subsequently purchase 

approximately 6 unique drugs per person. Across the different types of insurance coverage, the 

increase in unique drugs ranges from 0.9 (19%) to 1.2 (26%). In contrast, our model with the 7 

chronic conditions results in predicted increases in the number of unique drugs purchases that are 

smaller, ranging from 0.6 (Medigap) to 1.0 (employer-based coverage). 

Estimated Effects of Insurance by Therapeutic Class 

Prescription drug coverage from Medicaid, employer-based plans, and Medicare HMOs 

is also associated with statistically significant increases in total expenditures for many 

therapeutic drug classes (Table 6). For example, our models predict that beneficiaries without 

drug coverage would increase their total expenditures for analgesics and gastrointestinal agents if 

they gained coverage through an employer-based plan. The greatest estimated increase in 

expenditures per person associated with employer-based coverage is for gastrointestinal agents 

($69), largely due to an increase in spending on proton pump inhibitors ($57). We also estimate 

a $39 increase in analgesics, most of which would be spent on cyclo-oxygenase-2 (COX-2) 

16
 
 

inhibitors ($26). In addition, employer-based coverage is associated with increased expenditures 

for sex hormones ($13). 

We find that Medicare HMO coverage is associated with increases in spending on 

cardiovascular ($119) and hormone ($41) drugs. It is interesting that Medicare HMO coverage 

is not associated with any increase in spending on analgesics. This might be the result of HMO 

restrictions on the prescribing of COX-2 inhibitors. COX-2 inhibitors are more expensive but 

not clearly more effective in relieving pain than traditional nonsteroidal antiinflammatory drugs 

(NSAIDS) such as ibuprofen (Chou et al, 2006). 

Finally, Medicaid coverage is associated with large increases in expenditures per user for 

cardiovascular drugs ($162), gastrointestinal drugs ($126), and analgesics ($79). Within the 

category of cardiovascular drugs, Medicaid coverage is associated with a large increase in 

antihyperlipidemics ($107). 

DISCUSSION 

The advent of Part D in 2006 increased access to prescription drugs for many Medicare 

beneficiaries. Early reports indicated that most seniors had prescription drug coverage as of 

2006, including relatively generous coverage through employer-sponsored plans and low-income 

subsidy Part D plans, and less generous coverage from Medicare Advantage and standard Part D 

plans. Nonetheless, Neuman et al. (2007) estimated that 8.5 percent of seniors lacked drug 

coverage after the annual 2006 Part D enrollment period, and up to an additional 8 percent of 

seniors may not have had creditable drug coverage which meets or exceeds the actuarial value of 

the standard Part D benefit. In our study, we use data from prior to Part D to predict its effects 

on individual level expenditures and utilization of drugs. We estimate that the effect of coverage 

17
 
 

 

through Medicare HMOs and employer-based coverage is associated with an increase in drug 

expenditures, on average, of approximately 15 to 20 percent, or roughly $217 to $288 per 

beneficiary (2006 dollars). We also estimate that increased coverage from Medicaid, employerbased, or Medicare HMO plans resulted in a 20 percent increase, from 5 to 6, in the number of 

unique drugs used by previously uncovered beneficiaries. Our estimates of the impact of drug 

coverage on aggregate use and expenditures fall in between two recent studies (higher than Khan 

et. al. (2007) but lower than Shea et. al. (2007). 

We also find that Medicaid coverage is associated with much larger increases in marginal 

expenditures on prescription drugs. Our model estimates a marginal annual increase in total 

prescription drug expenditures of approximately $480 (33 percent) with Medicaid compared to 

having no coverage. This estimate may be relevant to beneficiaries who gained coverage 

through a Part D plan and received a low-income subsidy that covered premiums and 

copayments. As of January 2007, approximately 9.2 million enrollees (seniors and disabled) 

were receiving the low-income subsidy (Kaiser Family Foundation 2007). However, many 

seniors eligible for the low-income subsidy were instead enrolled in a standard (non-subsidized) 

Part D plan or they had no drug coverage at all (Government Accountability Office, 2007; 

Neuman et. al., 2007). Our results emphasize that access to prescriptions drugs could be 

substantially improved for the millions of seniors who were eligible for, but not enrolled in, the 

Part D low-income subsidy. The Government Accountability Office (2007) contains 

recommendations for improving outreach efforts for the Part D low-income subsidy program. 

There are some limitations to this study. We assume that more generous coverage will 

result in higher drug expenditures, and address the likely endogenous relationship between 

insurance and prescription drug expenditures by including 2 variables that capture beneficiaries’ 

18
 
 

 

attitudes towards risk and use of health services and controlling for observed health status. 

However, differences in the effects of various drug insurance categories may also be due to 

unobserved differences among the beneficiaries in these categories that we are unable to control 

for. In addition, differences may also be due to formulary restrictions and other plan policies that 

are not reflected in our data. Finally, sample size limitations may explain why Medigap 

coverage was not associated with statistically significant increases in drug expenditures. 

The 2 most generous forms of coverage—Medicaid and employer-based—are associated 

with increased purchases of prescription analgesics. Most of the estimated increase in analgesics 

is for COX-2 inhibitors, which are more expensive but not necessarily more effective than 

traditional NSAIDs (Chou et al., 2006). This finding is important because it illustrates that 

relatively generous coverage could result in some seniors replacing traditional drugs (for 

instance, traditional NSAIDs) with newer but not necessarily more effective drugs (COX-2 

inhibitors) to treat the same medical conditions. For example, traditional analgesics and drugs for 

Type 2 diabetes have been shown to be as effective as newer, more expensive agents (Chou et al. 

2006; Bolen et al. 2007). These findings may be used to inform Part D benefit design and 

formularies. 

19
 
 

 

NOTES 

1.	 Expenditure data from the Medical Expenditure Panel Survey (MEPS) for 2006 is scheduled 

for release on November 2008, according to the MEPS data release schedule available from 

the MEPS website (http://www.meps.ahrq.gov). The Medicare Current Beneficiary Survey 

Cost and Use file for 2006 is scheduled for release on or after March/April 2009, according 

to the Research Data Assistance Center (ResDAC). 

2.	 The sample of Medicare beneficiaries in Shea et al. (2007) excluded people with prescription 

drug coverage for part of the year and people with drug coverage who were not enrolled in a 

Medicare supplemental insurance plan. Shea et al. (2007) did not indicate baseline 

prescription drug use for sample members without prescription drug coverage. 

3.	 The 19 therapeutic drug classes were also selected based on sufficient sample size to estimate 

conditional expenditures. 

4.	 The point-in-time measure of insurance coverage allowed us to include a measure of 

Medicare HMO coverage that benchmarks well to administrative enrollment data from the 

Center for Medicare & Medicaid Services. 

5.	 We also estimated two-part gamma models. We present estimates from two-part Poisson 

models based on results from the specification tests (a variation of a Park test on the 

residuals) and sensitivity analyses we conducted. We also estimated one-part models, 

because more than 90 percent of elderly beneficiaries have positive drug expenditures. The 

one- and two-part Poisson models yield similar results, so for ease of exposition, we present 

estimates based on the two-part Poisson models. 

20
 
 

6.	 Both approaches to correcting for endogeneity are useful, but neither is definitive. In the 

fixed effects model used by Khan et. al. (2007), the model is identified by within panel 

changes in insurance status and validity relies on the assumption (which Khan et. al. (2007) 

provide some evidence to support) that these changes are exogenous. Similarly, the validity 

of the residual inclusion model used by Shea et. al. (2007) relies on the quality of their 

instrumental variables. 

21
 
 

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23
 
 

Total Expenditure 

Type of supplemental population Mean Median at the 95th 

insurance (millions) expenditure expenditure percentile 

 

Medicaid 2.6 

(0.2) 

2,174** 

(102.0) 

1,298** 

(83.7) 

7,068**

(324.1) 

 

 Employer-based with 9.2 1,712** 1,115** 5,448* 

prescription drug coverage (0.4) (58.9) (49.3) (230.6) 

 

Medicare HMO 4.1 

(0.3) 

1,556 

(69.9) 

1,038 

(94.9) 

4,775

(251.1) 

 

 Medigap with prescription 1.5 1,619 1,321** 4,724 

drug coverage (0.1) (103) (155.6) (377.7) 

 

 Veterans’ Administration or 

state/local program1 

3.2 

(0.2) 

1,768** 

(102) 

1,143** 

(81.7) 

4,886 

(394.4) 

 

No prescription drug 

2 coverage

12.9 

(0.5) 

1,456 

(49.9) 

865 

(39.4) 

4,937 

(173.4) 

 

All elderly Medicare 

33.4 

(1.0) 

1,632** 

(32.6) 

1,043** 

(24.9) 

5,314**

(117.9) 

Sample size = 7,036 

 

TABLE 1. Prescription drug expenditures by supplemental insurance status, 

Medicare beneficiaries age 65+ 

Average annual estimates in 2006 U.S. dollars 

Source: Authors' calculations from the 2000-2003 Medical Expenditure Panel Survey, Agency for 

Healthcare Research and Quality. 

Notes: 

1.	 Beneficiaries are assigned to these categories using source of payment information. 

2.	 Includes beneficiaries with no supplemental coverage and beneficiaries with employer-based 

or Medigap private supplemental insurance that does not include prescription drug coverage. 

* Estimate is different from the estimate for beneficiaries with no drug coverage at p < 0.05. 

** Estimate is different from the estimate for beneficiaries with no drug coverage at p < 0.01 

24
 
 

 

 

 

 

 

TABLE 2. Demographic, socioeconomic, and health status characteristics by type of 

supplemental coverage, Medicare enrollees age 65+, 2000-2003 

Employer Medigap 

based with with No 

prescription Medicare prescription prescription 

Characteristic Medicaid coverage HMO coverage coverage 

Age category 

65-74 49.1 62.8* 50.5 59.0* 47.7 

75-84 31.9 27.0* 34.2 26.1* 34.0 

85 and older 19.0 10.2* 15.3 14.9 18.3 

Race/ethnicity 

Hispanic 20.0* 2.7* 8.8* ^ 4.3 

Black 20.8* 6.9 3.9* 4.5* 8.3 

Asian/Other 10.0* 2.1 3.3 ^ 2.6 

White 49.3* 88.3* 84.0 90.1* 84.8 

Gender 

Male 31.8* 50.6* 38.9* 38.7* 27.2 

Education 

Less than high school 63.2* 20.3* 31.5 26.1* 34.3 

High school/Graduate 

Equivalency Degree 28.7* 50.5 51.4 52.0 49.4 

College 6.4* 19.1* 12.4 17.0 12.3 

Advanced degree 1.6* 10.0* 4.6 ^ 3.9 

Income category (% poverty 

line) 

< 124% 47.2* 8.1* 15.3* 11.6* 20.1 

125-199% 23.0 13.1* 25.7 25.5 25.3 

200-399% 19.9* 35.9 36.9 36.0 33.0 

≥ 400% 9.9* 42.9* 22.1 26.9 21.6 

Died or entered institution 

during the year 8.8 2.8* 3.1* ^ 7.2 

Self-rated health 

Fair or poor physical 

health 45.1* 23.0 25.6 16.3* 24.1 

Fair or poor mental health 19.9* 7.5* 8.6 5.2* 10.2 

Activities of Daily Living

 (ADL) difficulties 16.1* 4.0* 4.5* 5.3 7.4 

Instrumental Activities of

 Daily Living (IADL) 

difficulties 30.3* 8.3* 10.8 9.8 13.8 

Limited physical 

functioning 53.7* 31.9* 33.9 34.1 36.8 

25
 
Employer Medigap 

based with with No 

prescription Medicare prescription prescription 

Characteristic Medicaid coverage HMO coverage coverage 

Limited cognition 22.7* 8.0* 9.6 9.6 11.4 

Attitudes towards risk 

Willing to take risks 14.0 10.7* 13.8 14.2 13.3 

Can overcome illness 

without 

 medical professional 10.1 9.3* 13.0 9.4 12.7 

Geographic variables 

Northeast 22.2 23.8 23.4 21.6 19.7 

Midwest 13.7* 25.1 13.9* 22.5 26.1 

South 39.9 33.2* 24.4* 37.9 39.6

West 24.1* 17.8 38.3* 18.0 14.6 

Metropolitan Statistical 

Area 76.3 80.3* 91.6* 73.0 71.6 

Health conditions: has a doctor 

ever told you that you have: 

Diabetes 23.3* 14.8* 17.3* 11.6 12.3

Hypertension 57.5* 55.3* 57.4* 50.7 51.3

Heart disease 27.3 32.3* 28.5 27.0 26.2 

Asthma 11.7* 8.6 8.0 7.6 7.8 

Arthritis 42.3* 39.5 36.9 39.4 35.7 

Stroke 11.8* 9.4 10.9 8.1 8.5 

Emphysema 5.8 5.1 4.5 5.1 4.4 

Sample size = 7,036 

 

Source: Authors' calculations from the 2000-2003 Medical Expenditure Panel Survey, Agency 

for Healthcare Research and Quality. 

Notes: 

* Estimate is different from the estimate for beneficiaries with no drug coverage at p < 0.05, or 

better. 

^ Unreliable estimate, relative standard error > 0.30.
 

26
 
 

 

 

 

 

TABLE 3. Out-of-pocket prescription drug expenditures by type of coverage 

Medicare beneficiaries age 65+, 2000-2003. 

Employerbased with Medigap 

with No 

Medicaid 

prescription 

drug 

coverage 

Medicare 

HMO 

prescription 

drug 

coverage 

prescription 

drug 

coverage 

Percentage paid out-of-pocket for 

drugs1 

With current coverage 24.5 33.4 53.8 52.9 87.9 

With Part D coverage2	 49.4 49.8 50.6 49.8 49.5 

Source: Authors' calculations from the 2000-2003 Medical Expenditure Panel Survey, Agency for 

Healthcare Research and Quality. 

Notes 

1.	 For each insurance category, the percentage of total expenditures paid out-of-pocket was 

calculated as: 

∑(Out-of-pocket prescription expenditures) / ∑ (Total prescription expenditures) 

2.	 The Part D coverage estimates were produced by assuming that beneficiaries in each insurance 

category instead had prescription drug coverage from a standard Part D plan. 

3.	 Out-of-pocket costs for seniors with no coverage are less than 100 percent because some seniors 

may have obtained drug coverage after our point-in-time measure of insurance coverage, or 

because some seniors received payment for prescription drugs from sources (such as automobile 

insurance companies) that we do not classify as prescription drug coverage. 

27
 
 

 

 

 

 

 

 

 

 

TABLE 4. Generalized Linear Model (Two-part Poisson Model with Log Link) 

 Regression Coefficients of Prescription Drug Expenditures, without chronic condition 

control variables 

Logit Standard Poisson Standard 

Characteristic Coefficient error Coefficient error 

Probability of Use Conditional Expenditures 

(Part 1) (Part 2) 

Type of supplemental insurance 

Medicaid 0.412 0.224 0.251** 0.062 

 Employer-based with drug coverage 0.925** 0.140 0.116* 0.048 

Medicare HMO 0.872** 0.171 0.076 0.060 

Medigap with drug coverage 0.685* 0.308 0.087 0.070 

Age Category 

75 - 84 0.383** 0.128 0.010 0.041 

85 and older 0.555** 0.163 -0.150** 0.042 

Race/ethnicity 

Hispanic -0.276 0.194 -0.092 0.061 

Black -0.355 0.186 -0.042 0.054 

Asian/other -0.372 0.290 -0.187 0.099 

Education 

High school/Graduate Equivalency Degree 0.036 0.137 0.041 0.041 

College 0.194 0.199 -0.016 0.053 

Advanced degree 0.290 0.275 -0.090 0.067 

Gender 

Male -0.731** 0.117 -0.091* 0.037 

Income category (% poverty line) 

125 - 199% 0.088 0.169 0.095 0.051 

200 - 399% -0.044 0.158 0.049 0.044 

> 400% 0.090 0.191 0.016 0.051 

Self-rated health 

Fair or poor physical health 0.911** 0.173 0.365** 0.047 

 Fair or poor mental health -0.134 0.246 0.047 0.063 

28
 
 

 

 

 

 

 

Characteris 

Logit Standard Poisson Standard 

tic Coefficient error Coefficient error 

ADL difficulties -0.258 0.320 0.127 0.067 

IADL difficulties 0.311 0.275 0.121* 0.056 

 Limited physical functioning 0.982** 0.157 0.284** 0.041 

 Limited cognition -0.340 0.233 0.034 0.054 

Patient attitudes 

 Willing to take risks -0.024 0.150 0.035 0.050 

 Can overcome illness w/o medical professional -0.921** 0.152 -0.254** 0.070 

Geographic variables 

Midwest -0.290* 0.136 0.086 0.058 

South -0.127 0.137 0.012** 0.052 

 West -0.277 0.164 -0.211 0.055 

 Metropolitan Statistical Area -0.245 0.125 -0.034 0.039 

Panel 

Panel 6 (2001) 0.267 0.143 0.084 0.049 

Panel 7 (2002) 0.492* 0.164 0.213** 0.054 

Panel 8 (2003) 0.391* 0.162 0.287** 0.051 

Died or entered an institution during the year -1.502** 0.231 -0.594** 0.127 

Missing value for attitude variable -0.472** 0.154 -0.168** 0.053 

Constant term 1.912** 0.253 7.090** 0.082 

Sample size = 5,772 

Source: Authors’ calculations from the 2000 – 2003 Medical Expenditure Panel Survey, Agency for 

Healthcare Research and Quality. 

Notes: 

* (**) Significantly different from 0 at p < .05 (p < .01) 

The coefficients and standard errors take into account sampling weights and the complex design of the 

MEPS to yield nationally representative estimates of the non-institutionalized Medicare population. 

29
 
Employer- based 

No drug Coverage with drug Medi

Model specification (Baseline) Medicaid coverage HM

Average 

Poisson with log link expenditures Marginal increase in expenditu

conditions2 $1,453 480* 288* 217

No chronic (124) (81) (94

$1,453 392* 195* 11

Chronic conditions (108) (72) (92

Average number 

of purchases Marginal increase in number of uniq

No chronic conditions2 4.7 1.0* 1.2* 0.9Chronic conditions 4.7 0.8* 1.0* 0.7TABLE 5. Change in per capita annual drug expenditures associated with gaining drug coverage (2006 dollars) Source: Authors’ calculations from the 2000 – 2003 Medical Expenditure Panel Survey, Agency for Healthcare Notes: 1. Simulated change in average expenditures (number of unique drugs) if persons with no prescription drug cove

under each insurance category. To estimate the marginal effects of drug coverage, we use results from our mode

30
predicted use and expenditures for the uninsured when they are uncovered and when they are simulated to have e

insurance. The marginal effect for each type of insurance is the difference between these estimates averaged acrpersons. The control variables are listed in Table 2. 2. The chronic conditions included in some simulations and not in others are: diabetes, hypertension, heart disea

stroke, and emphysema. *Statistically significant at p < .05, or better. Standard errors (in parentheses) were estimated using the method of balanced repeated replicates (BRR).

31
 

 

 

 

 

 

 

 

 

 

 

 

 

 

TABLE 6. Changes in per capita annual prescription drug use and expenditures associated with 

gaining drug coverage, by therapeutic drug class (2006 dollars) 

Employer- Medigap 

based with 

No drug with drug Medicare drug 

coverage Medicaid coverage HMO coverage 

Baseline: 

average 

Therapeutic class expenditure Marginal increase in expenditures 

Cardiovascular 580 162* 56 119* 31

 Antihypertensives 331 54 45 71* 17 

Antihyperlipidemics 186 107* 10 45 -5 

Hormones 213 47 41 54 97

 Antidiabetic 97 33 20 3 42

 Sex hormones 32 17 13* 41* 13

 Bisphosphonates 38 -7 6 -8 13

 Thyroid drugs 23 -1 -1 1 11 

Gastrointestinal 112 126* 69* 13 -11

 Proton pump inhibitors 76 100* 57* -5 -16

 H2 antagonists 24 16 6 17 0 

Psychotherapeutic 101 12 7 3 34

 Antidepressants 57 13 7 0 23

 Sedatives, anti-anxiety 31 1 0 -2 -2 

Analgesics 97 79* 39* 18 34 

Cyclo-oxygenase-2 

(COX-2) inhibitors 53 53* 26* 0 33

 Nonsteroidal anti-

 inflammatory drugs (NSAIDs) 16 16* 12* 11 0

 Narcotic analgesics 17 17 7 15 2 

Anti-infectives 34 7 15 -3 -8 

Source: Authors' calculations from the 2000-2003 Medical Expenditure Panel Survey, Agency for 

 Healthcare Research and Quality. 

Notes: 

*Statistically significant at p < .05, or better. 

Expenditures for therapeutic classes were estimated using two-part Poisson models that control for 

demographic, socioeconomic, self-rated health status and attitude variables. 

32