Evaluating an Alternative Data Source for Editing MEPS Drug Prices 

Marc W. Zodet, Steven C. Hill, and Samuel H. Zuvekas 

Agency for Healthcare Research & Quality 

540 Gaither Road 

Rockville, Maryland 20850 

marc.zodet@ahrq.hhs.gov 

steven.hill@ahrq.hhs.gov 

samuel.zuvekas@ahrq.hhs.gov 

Disclaimer 

The views expressed are those of the authors and no official endorsement by the Agency for Healthcare Research & Quality 

or the Department of Health and Human Services is intended or should be inferred. 

I. Introduction 

The Medical Expenditure Panel Survey (MEPS) Prescribed Medicines (PMED) file is a unique data resource that provides 

detailed information on prescription drug use and expenditures for a nationally representative sample of persons in the U.S. 

civilian, noninstitutionalized population. When it was initially fielded in 1996, the MEPS was the first national survey to 

collect detailed information on drug purchases from pharmacy providers frequented by household sampled persons. This 

information, which is collected by a linked survey of pharmacy providers, includes the quantity purchased, the total retail 

price, the total amount paid by source of payment, and the pharmacology of each drug, as summarized by the National Drug 

Code (NDC). Constructing the MEPS PMED files using both household- and pharmacy-reported data involves many 

complex data editing and matching tasks (Moeller et al. 2001). Data quality is critical because the MEPS is used for national 

estimates, behavioral modeling, and policy simulations, including analyses of prescription drugs. This study is part of 

ongoing efforts by staff in AHRQ’s Center for Financing, Access, and Cost Trends (CFACT) to continually assess and 

improve the quality of the editing process for the MEPS prescribed medicine data. 

A key aspect of data quality is identifying implausible outliers in the payment data. Outlier prices may be caused by 

transcription errors in reported prices or quantities, by incomplete reporting of payments, or by other reporting errors. The 

goal of editing drug prices in the MEPS PMED files is to increase the overall quality of the data while accurately 

representing the dispersion in prices that occurs in the retail market for drugs. The average wholesale price (AWP) was the 

benchmark adopted in 1996 to identify outlier prices in the MEPS, and is still used today. The AWP is an NDC-level list 

price for drugs sold by wholesalers to retail pharmacies. Although in practice the AWP does not necessarily reflect what 

pharmacists pay for drugs, it has been used as a basis for payments to retail pharmacy providers by both public and private 

insurers (Congressional Budget Office 2007). To screen for price outliers, each retail unit price (RUP) in the pharmacy data 

is compared with the average wholesale unit price (AWUP) for that NDC. For most drugs, units are pills, but they can also 

be milliliters for liquids, etc. Outlier prices are then edited by adjusting the reported price or quantity (Moeller et al. 2001). 

The central motivation for examining an alternative to the AWP is that the relationship between RUP and AWUP may have 

changed since the beginning of MEPS. There are two specific reasons for concern. First, since the AWP is a list price (not 

an average of transaction prices) it may be subject to manipulation and strategic behavior that could alter the AWUP-RUP 

relationship. In fact, a trial court found that three pharmaceutical companies caused the publication of inflated AWPs for 

some drugs, leading to settlement agreements to change the methods used for calculating some AWPs (United States District 

Court District of Massachusetts 2007). It remains uncertain, however, to what extent private and public insurers may have 

adjusted their payment methods in response to changes in the AWP. Second, studies have shown the relationship between 

RUP and AWUP varies by the patent status of the drug. In particular, for single source drugs, AWP was shown to be a good 

predictor of pharmacy acquisition costs, and ultimately of retail prices. Pharmacy acquisition costs averaged about 80 

percent of AWP for single source drugs and this percentage showed relatively little variation across drugs. By contrast, 

pharmacy acquisition costs for generic drugs averaged about 30 to 40 percent of AWP and there was substantial variation in 

this percentage across drugs (Congressional Budget Office 2007, Levinson 2005, Sugerman-Broznan and Woolman 2009). 

Separating plausible prices from outliers is more difficult for generics than brand name drugs due to the greater dispersion in 

the distribution of generic prices around their AWP.
The purpose of this study is to assess the feasibility of replacing AWUPs with median retail prices estimated from the 

MarketScan Outpatient Pharmaceutical Claims database. We use medians rather than means to avoid undue influence from 

outliers. Because the MarketScan data contain adjudicated claims for millions of people with private employment-related 

insurance, we assume they provide an accurate and valid benchmark for the MEPS pharmacy-reported drug pricing for the 

MEPS sample representing the population with private employment-related insurance. However, the MEPS includes persons 

with other sources of prescription drug coverage as well as the uninsured. Thus, the first component of our evaluation is to 

determine the extent to which MarketScan price data can be generalized to other populations. While we do not have data for 

all payers, we are able to compare the distribution of prices in the MarketScan and the Medicare Part D program 

(unfortunately, the Medicare administrative data are not available in time to use directly to edit the MEPS). For each 

acquisition in both the MarketScan and Medicare claims, we calculate the ratio of retail unit price to the observed NDC-level 

median retail unit price. We then compare the distributions of this price dispersion observed in both data sets separately for 

brand name and generic drugs. 

 

In the second component of our evaluation, we determine whether thresholds that suggest outlier unit prices derived from 

retail prices outperform thresholds derived from AWP. To derive the outlier thresholds based on retail prices, we use 

validation data for a sample of MEPS Medicare beneficiaries matched to their Medicare claims. Here, we take advantage of 

the fact that we can compare the price as measured in MEPS to the true retail price for each prescription drug for persons in 

this validation sample. The newly derived editing rules based on median RUP and the previous rules based on AWUP are 

both set to maximize the accuracy of the MEPS data. Nonetheless, our expectation is that using the median RUP rather than 

the AWUP would yield greater accuracy, particularly for generics. In particular, we expect that the distribution of prices 

around the median RUP has less dispersion than the distribution of prices around the AWUP. If so, then the rules using the 

median RUP would better differentiate plausible prices and outliers, thereby facilitating the creation of more accurate data. 

Therefore, we implement the two sets of editing rules on the validation data set and compare the accuracy of the resulting 

prices. 

II. Previous Evaluations 

Two prior studies have evaluated the quality of the MEPS prescription drug data. First, Zodet et al. (2009) provided a 

detailed examination of the process of identifying and editing outliers in the retail prices reported by pharmacy providers. 

The purpose of that study was to determine whether the editing rules implemented for the 1996 MEPS data were still valid 

for the 2006 and subsequent years of MEPS prescribed medicines data. The central strategy of that exercise was to compare 

distributions of RUPs in the 2006 MEPS data to distributions of RUPs reported in the 2006 MarketScan data. Observed 

discrepancies in acquisition prices between the MEPS and MarketScan were traced to the editing rules for identifying price 

outliers in the MEPS Pharmacy Component (PC). Based on these findings we proposed modifications to the MEPS price 

editing rules that more accurately reflected the current RUP-AWUP relationship overall, and by patent status, while 

continuing to benchmark to other national data sources. The new editing rules were implemented beginning with the 2007 

MEPS. 

The second study, summarized in Hill et al. 2011, assessed these revised editing procedures using a sample of MEPS 

Medicare beneficiaries matched to their Medicare Part D data. Both total drug expenditures for matched beneficiaries and 

prices for matched drug acquisitions were similar across the data sets, but additional refinements were identified to further 

improve the data. These changes were implemented with the 2009 MEPS. However, the new rules continue to rely on the 

AWUP and have not alleviated our concerns that 1) this metric may be subject to manipulation and strategic behavior that 

could alter the RUP-AWUP relationship and 2) the correlation between RUP and AWUP is weak for generic drugs. 

III. Methods 

A. Overview 

Since the inception of the MEPS in 1996 outlier drug prices have been identified by examining the distribution of the ratio of 

RUP and AWUP (i.e., RUP/AWUP) which we have referred to as the PRATIO. Unit drug prices are considered outliers if 

their PRATIO falls outside of established thresholds (i.e., less than or greater than established lower and upper boundaries). 

Changes were made to these threshold levels based on the two previous evaluations. We now evaluate whether outlying drug 

prices can be identified as well or better if we utilize a median RUP rather than the AWUP. We perform two analytic tasks to 

do this evaluation. First, we assess the reasonableness of using the MarketScan (private payers) data as a proxy for Medicare 

payers. In doing so, we compare the RUP/median RUP ratio from the MarketScan data to the ratio from the Medicare 

prescription drug claims provided by the Centers for Medicare & Medicaid Services (CMS). Second, we evaluate the 

potential impact of any rule modifications brought about by the outlier thresholds suggested by the new price to median price 
ratio (i.e., RUP/median RUP). As part of this phase we assess the accuracy of the MEPS price data and compare resulting 

average retail prices derived under both sets of editing criteria (i.e., RUP/AWUP and RUP/median RUP). 

 

B. Data 

We use four data sets of retail drug purchases in 2007: the MarketScan Outpatient Pharmaceutical Claims data, a sample of 

Medicare prescription drug claims, pharmacy reported drug purchases collected as part of the MEPS pharmacy follow-back 

survey, and pharmacy reported purchases for a sample of MEPS Medicare beneficiaries who are matched to their Medicare 

claims records. 

MarketScan 

The 2007 MarketScan Outpatient Pharmaceutical Claims data contain approximately 198 million adjudicated drug claims for 

more than 16 million people. As such, we assume these data to be accurate and valid and that they reliably capture retail drug 

price. These data come from a larger set of databases collectively referred to as the MarketScan Databases. The MarketScan 

databases capture, from private insurance claims records, person specific information for multiple dimensions of health care 

services (e.g., clinical utilization, expenditures, insurance plan enrollment, etc.). The data comprise people with private, 

employment-related insurance coverage, predominantly from large employers. The employers are, however, a convenience 

sample. For this analysis we selected a 10 percent sample of the outpatient pharmacy claims. These acquisition-level data 

include the price paid for the medication and the quantity dispensed. These are final claims, reflecting any adjustments to 

payments by the insurers to the pharmacies. The recorded prices are the retail prices paid to pharmacies, which is the same 

price concept sought by the MEPS PC. 

Using the MarketScan data the median retail unit price was calculated for NDCs with at least 100 acquisitions. We then 

calculated the retail unit price to median retail unit price ratio (i.e., RUP / median RUP). Computation of this ratio did result 

in some missing values. These occurred for NDCs with missing RUP information or when the median RUP was zero (i.e., 

$0.00). In a production setting we would sample claims within NDCs to minimize NDCs with insufficient sample. Patent 

status for each NDC in the MarketScan data is provided by MarketScan from the RED BOOKTM. Some NDCs did change 

patent status during the year. A side analysis demonstrated that the median RUP was not sensitive to these changes. Thus, 

NDCs with multiple patent statuses were characterized by their modal status. After excluding NDCs with fewer than 100 

purchases, NDCs not found in the MEPS pharmacy follow-back survey, and records with missing RUP to median RUP 

ratios, the analytic data file contained approximately 17.6 million adjudicated prescribed medicine claims. 

The NDC-level median RUP is derived from the MarketScan data and is used as the denominator for all price to median price 

ratios calculated for each data source. 

Medicare Claims CMS provided us claims data for a five percent random sample of Medicare beneficiaries. These claims 

files contain drug claims from all plans in the Medicare prescription drug program. As with the MarketScan data, prices are 

the amounts paid to pharmacies. 

MEPS PC MEPS data for this project come from the 2007 MEPS Pharmacy Component (MEPS-PC). The MEPS-PC is a 

survey of pharmacy providers identified by household respondents during the series of MEPS Household Component (HC) 

interviews. For each round of the HC, respondents are asked to provide the name(s) of any prescribed medication that they 

or their family members purchased or obtained during the reference period for the survey round. Various information 

regarding reported medications is collected: 1) whether or not any free samples of the medication were obtained, 2) the 

name(s) of any health problem(s) for which the medication was prescribed, 3) the number of times the medication was 

obtained/purchased, 4) when the person first used the medication, and 5) the names, addresses, and types of pharmacies that 

filled the prescriptions. During the third and fifth round interview, unless all their acquisitions were free samples, 

respondents are asked to sign permission forms authorizing contact with these pharmacies and the release of their pharmacy 

records. The pharmacies for sample members who grant this permission are then sent a follow-back mail survey. 

The mailing sent to the pharmacies includes a cover letter from the Department of Health and Human Services briefly 

describing the scope of the study, a printed listing of the persons for whom information is requested as well as copies of their 

signed permission forms, and instructions on how to provide the data. The pharmacies are asked to provide information 

about each prescription filled or refilled for each listed patient. 

The pharmacies can provide the data by either submitting a detailed computer printout for each drug acquisition or by 

completing a questionnaire booklet for each acquisition. A few large chains provide electronic data. (Starting with the 2009 

data cycle, some pharmacies provided information to a data collection specialist on the phone.) The requested data include: 
1) the date the prescription was filled or refilled, 2) the NDC, 3) the medication name, 4) the strength of the medicine, 5) the 

quantity dispensed, 6) sources of payment, and 7) the payment amount made by each source. 

The data collected from the pharmacies are ultimately matched to the prescribed medicines listed by respondents in the HC. 

The payment data obtained in the PC are the primary source of price information for the matched HC acquisitions. The PC 

data are also used to impute payment data for HC acquisitions not matched to PC acquisitions. Detailed documentation on 

how the HC and PC data are matched, as well as imputation strategies, is provided in Moeller et al. (2001). 

We utilize a production version of the 2007 MEPS PC, an acquisition-level file (i.e., one record for each fill/refill of a 

prescription from the pharmacy). The file includes the retail unit price variable (RUP), constructed as the sum of payments 

for the prescription divided by the quantity dispensed (e.g., number of pills or milliliters). 

As part of our efforts to assess the effects of using the MarketScan median unit price, we use this file to evaluate the impact 

of alternative rules on prices. While estimates based on this file are not nationally representative, they have in previous 

analyses (Zodet et al. 2009) suggested the direction of potential effects on the data ultimately released to the public. Median 

RUPs from the MarketScan data were merged onto the MEPS PC file by matching NDCs. The 2007 MEPS PC has 212,705 

records, and 192,304 records (90% of all records) with median prices from the MarketScan data are used in the analysis. 

MEPS PC linked to Medicare Claims The fourth analytic file is based on a sample of MEPS-HC Medicare beneficiaries 

linked with Medicare claims. This validation sample was constructed using the 2007 MEPS PC and their matched Medicare 

claims from CMS (a separate sample from the above mentioned 5 percent) and was used to assess the effects of alternative 

rules on data accuracy. Under a Data User Agreement between AHRQ and CMS, beneficiaries from the MEPS HC were 

matched to Medicare enrollment data. (Details are in Hill et al. 2011.) Among the matched beneficiaries in Part D, the fills 

and refills reported by the PC pharmacies were matched to those reported by the Part D plans. Date, NDC, therapeutic class, 

drug, dose form, strength, and drug name were used to match the fills. Median RUPs from the MarketScan were merged 

onto the linked file by matching NDCs. The sample has 18,001 matched fills with median unit prices from MarketScan. We 

study the 15,375 fills that have complete or partial payment data from the PC, and the 2,626 lacking any payment data are 

excluded. The matched file with complete or partial data, however, has only 7% of all PC records. 

C. Analytic Methods 

The first phase of our study is to assess the reasonableness of using the MarketScan data (i.e., private claims) as a proxy, not 

only for private payers, but also Medicare payers. To address this part of the analysis we use methods that are purely 

descriptive in nature. For each data source (i.e., MarketScan, MEPS PC, and Medicare), we calculate mean and median 

purchase prices across patent status and payer. We then plot density distributions to compare the distributions of the RUP to 

median RUP ratios observed in the MarketScan and Medicare claims data. 

The analysis for phase two, assessing the impact of alternative editing criteria on the accuracy of the PC data, relies on the 

sample of MEPS Medicare beneficiaries linked to their matched Medicare claims. We first report the accuracy of the 

unedited data using two criteria: (1) exact agreement on price between the MEPS-PC data and the Medicare claims, and (2) 

Lin’s concordance correlation coefficient. Lin’s concordance correlation coefficient combines measures of precision and 

accuracy for continuous variables, and it is scaled from −1 (perfect disagreement) to +1 (perfect agreement) (Lin 1989). We 

then assess the accuracy if the data were edited using thresholds determined using the AWUP and the median unit price. 

Comparing rules based on the AWUP and median unit price indicates which approach better differentiates between accurate 

and outlier prices. Thresholds for identifying outliers are determined separately for RUP / AWUP ratio and the RUP / 

median RUP ratio. Both sets of thresholds maximize the number of acquisitions for which the editing decision is correct. 

Specifically, the editing decision for the acquisition is correct if either (1) it is not flagged as an outlier and the MEPS-PC

price differs from the claims data price by less than 10% or $5, or (2) it is flagged as an outlier and the MEPS-PC price 

differs from the claims data price by more than 10% and $5. Conversely, incorrect editing decisions are those (1) where the 

pharmacy and claims data differ but the record is not edited, and (2) the pharmacy and claims data agree but the fills are 

nonetheless flagged for editing. The thresholds differ by patent status due to the differences in the dispersion of prices by 

patent status. They also differ by the apparent completeness of the data collected from the pharmacies. For example, outlier 

editing thresholds for underreported payments are higher for records missing third party payment amounts; meaning more of 

the data will be edited. The two measures of data accuracy, agreement rates and Lin’s concordance correlation, are then 

calculated for the data edited each way. Tests compare the accuracy of (1) the edited and unedited data and (2) the two sets 

of edited data. Bootstrapping is used to identify statistically significant differences in Lin’s concordance correlation 

coefficient between the two sets of editing rules. 
Finally, we compare prices under the alternative editing rules using the much larger MEPS-PC sample. We present mean 

prices overall and by patent status, and test for differences using a paired difference in means test. 

IV. Results 

Table 1 shows summary measures of retail price for prescribed medicines and the distribution of purchases across patent 

status for the MEPS PC, MarketScan, and Medicare data. The summary statistics for the MEPS PC data are displayed by 

sources of payment. The percent distributions of acquisitions by patent status are fairly consistent between the MEPS PC 

and MarketScan data, not only for purchases covered by private insurance, but also for those covered by Medicare and 

Medicaid. While there is some small degree of variation across data source and payer type, the majority of prescription drug 

purchases were for generic medications (56-63 percent of purchases). Approximately one-third of the drug purchases were 

for single source brand name drugs; the remaining 4-8 percent of total purchases were for originator drugs (i.e., brand name 

drugs for which the patent has expired; generally face direct competition from generics). 

Mean retail prices for single source and originator drugs were higher for MarketScan compared to the prices in the MEPS PC 

for people with private insurance data: $154 vs. $117 and $86 vs. $46 respectively. Within the MEPS PC, mean retail 

prices for single source drugs were lower for purchases covered by private payers than those covered by either Medicare or 

Medicaid: $117 vs. $128 and $180 respectively. The mean retail price for originator drugs was similar for Private and 

Medicaid covered purchases, but higher for Medicare purchases: $46 and $45 vs. $54. The MEPS average retail price for 

originator drugs paid for by Medicare was lower than what was observed in the Medicare claims data: $54 vs. $87. The 

mean retail price for Generic purchases was relatively consistent across data file/payment source. 

Figure 1 overlays Medicare and MarketScan densities of RUP/median RUP for single source (brand name) drugs. For the 

most part, the density distributions are very similar. The vast majority of prices are within 20 percent of the median unit 

price; most are even closer. The plot seems to demonstrate that prices paid by private payers are sometimes lower than what 

are paid by Medicare. Nonetheless, Figure 1 suggests that the retail unit price to median retail unit price thresholds 

established to identify outlier unit prices can be the same for Medicare and private insurance payers. Figures 2 and 3 show 

the overlaid densities for originator and generic drugs, respectively. As with Figure 1 these densities from the Medicare data 

and the MarketScan data are very similar. In particular, the overlaid distributions for generic drugs are nearly identical. 

Based on these three figures, we feel comfortable using the MarketScan median RUP as a quality benchmark and 

replacement for the AWUP, not only for drug purchases covered by private payers, but also for Medicare payers; especially 

for generic medications. 

Turning to the validation sample, the thresholds for lower outliers based on the AWUP and median RUP are shown in Table 

2. The thresholds vary by patent status and whether the pharmacy reported the third party payment amount was positive, zero, 

or missing. Fills with complete payment data from pharmacies have very low thresholds because payments reported by the 

pharmacies rarely disagree with those in the validation data. For fills with missing payment data, the reported out-of-pocket 

amounts seldom equal the total price in the validation data; hence the higher thresholds indicate most would be edited. Zero 

third party payment data may be accurate or arise when the pharmacy only reports an out-of-pocket amount. This is a more 

ambiguous situation, and the thresholds lie between those for complete and missing data. The thresholds are lower for 

generics than single source brand name drugs, because there is more price dispersion for generics. 

Very high thresholds identify the few fills for which the pharmacies reported overly high prices. These cases are rare, 

making it difficult to specify a precise threshold. For all patent statuses, the thresholds selected are RUP/AWUP>10 and 

RUP/median RUP>10. When only one pill was reported, the error is assumed to be in quantity, not price. Fills with 

acquisition prices less than $16 are not edited, because prices of $16 or less are consistent with pharmacies’ one-price generic 

programs, and dispensing fees may greatly exceed the wholesale costs of some drugs. For example, one chain pharmacy will 

fill a 90-day supply of selected generics for $15.99, while other chains charge less. 

Using the matched set of Medicare claims, Table 3 compares accuracy of the unedited prices and prices edited using both sets 

of outlier thresholds. Focusing on Lin’s concordance correlation coefficient, a broad measure of accuracy, the unedited 

prices are significantly less accurate than the edited prices, and editing results in highly accurate prices. Editing, however, 

slightly reduces the percentage of records with prices that exactly agree with the claims data. For single source brand name 

drugs, using the median RUP to edit prices results in a statistically significantly higher Lin’s concordance correlation 

coefficient. The sample of brand name originators is smaller (N=1,145), the two sets of editing rules have statistically similar 

Lin’s concordance correlation coefficients; although the exact match rate is slightly lower for the rules based on the median 

RUP. For generics, in terms of exact matches, the editing rules based on the median RUP from MarketScan result in 

statistically significantly greater accuracy than the editing rules based on the AWUP, but the difference is small and the 
difference in Lin’s concordance correlation coefficients is also small and not statistically significant. Under both sets of 

editing rules, generics remain less accurate than brand name drugs. 

Using the larger sample of MEPS PC data, Table 4 shows the potential impact of switching to rules based on the MarketScan 

median RUP on average prices. Mean generic prices would be unchanged. Mean brand name prices would fall by 3.1 

percent for single source drugs and 6.5 percent for originators. Overall, mean prices in the MEPS PC would be 2.7% lower. 

V. Conclusion 

Currently, the MEPS PC relies on the AWUP as a benchmark for identifying outliers. We assessed a potential alternative 

benchmark: median unit prices calculated from the MarketScan’s database of private claims. Both the AWUP and median 

RUP are at the NDC level. We calculated unit prices relative to the median in both MarketScan and Medicare claims data 

and showed graphically that the distribution of unit prices for people with private insurance in the MarketScan is similar to 

the distribution found among Medicare beneficiaries with Part D coverage. This suggests that the median RUP from the 

MarketScan may be useful for editing prices paid by two sources (private and Medicare) that paid for 59.8 percent of drug 

expenditures in the population covered by the MEPS in 2009. Then we compared the accuracy of editing rules based on the 

AWUP and median RUP using a sample of MEPS PC data validated by matching to Medicare Part D claims. In this selected 

sample, we found that using the median RUP instead of the AWUP would slightly increase the accuracy of edited prices for 

generics and single source brand name drugs. After applying both sets of editing rules to the MEPS prescription drug data, 

we found edited generic prices would not change, but the prices of brand name drugs would be somewhat lower. Based on 

these data from 2007, it would be feasible but not particularly necessary to change the unit price benchmarks for editing the 

MEPS. Changes would likely improve accuracy slightly, but increase processing costs and create a small break in the 

comparability over time in the prices of brand name drugs and aggregate expenditures. 
VI. References 

Congressional Budget Office. A CBO Paper: Prescription Drug Pricing in the Private Sector. The Congress of the United 

States: Congressional Budget Office, January 2007. 

e-MedTools. GenericMedList.com. Accessed March, 2008. 

Levinson DR, Inspector General. Medicaid Drug Price Comparison: Average Sales Price to Average Wholesale Price. 

Office of the Inspector General, Department of Health and Human Services, June 2005, OEI-03-05-00200.

Gencarelli DM. Average Wholesale Price for Prescription Drugs: Is There a More Appropriate Pricing Mechanism? NHPF 

Issue Brief no 775, June 7 2002, The George Washington University, Washington, DC. 

Hill SC, Zuvekas SH, and Zodet MW. “Implications of the Accuracy of MEPS Prescription Drug Data for Health Services 

Research.” Inquiry vol. 48, no. 3, fall 2011, 242-259. 

Lin L. 1989. A Concordance Correlation Coefficient to Evaluate Reproducibility. Biometrics 45(1):255–268. 

Medical News Today. “First DataBank Agrees To Tentative Settlement In Suit Over Prescription Drug Average Wholesale 

Prices.” October 10, 2006. http://www.medicalnewstoday.com/articles/53634.php

Moeller JF, Stagnitti MN, Horan E, et. al. Outpatient prescription drugs; data collection and editing in the 1996 Medical 

Expenditure Panel Survey (HC-010A). Rockville (MD): Agency for Healthcare Research and Quality; 2001. MEPS 

Methodology Report No. 12. AHRQ Pub. No. 01-0002. 

Sugerman-Broznan A and Woolman J. Drug Spending and the Average Wholesale Price: Removing the AWP Albatross 

From Medicaid's Neck. Health Care Policy Report. Volume: 13 Number: 36. The Bureau of National Affairs, Inc., 2009. 

United States District Court District of Massachusetts. In Re Pharmaceutical Industry Average Wholesale Price Litigation, 

Civil Action No. 01-12257-PBS, Findings of Fact and Conclusions of Law, June 27, 2007. 

Zodet M, Hill SC, and Miller GE. “Comparison of Retail Drug Prices in the MEPS and Market Scan: Implications for 

MEPS Editing Rules.” Agency for Healthcare Research and Quality Working Paper No. 10001, February 2010. 
Table 1. Characteristics of prescription pharmacy purchases 

Patent Status MEPS PC, 2007 

Percent distribution of purchases by

MarketScan, 2007 

1

patent status 

Medicare Claims, 2007 

Single Source 

Originator 

Generic 

Single Source 

Originator 

Generic 

Single Source 

Originator 

Generic 

Privately Insured 

32.3% 

8.3% 

55.9% 

Medicare 

30.5% 

7.0% 

59.0% 

Medicaid 

29.9% 

5.6% 

58.6% 

MEPS PC, 2007 

Mean SD Median 

33.7% 

4.8% 

57.3% 

Summary of retail prices 

MarketScan, 2007 

Mean SD Median 

Medicare 

Mean

31.5% 

4.0% 

62.9% 

Claims, 2007 

SD Median 

Single Source 

Originator 

Generic 

Single Source 

Originator 

Generic 

Single Source 

Originator 

Generic 

Privately Insured 

$117 $182 $88

$46 $108 $21

$22 $89 $11

Medicare 

$128 $156 $97

$54 $80 $22

$20 $32 $11

Medicaid 

$180 $276 $112

$45 $58 $29

$20 $34 $12

$154

$86

$25

$243

$121

$44

$102

$41

$13

$137

$87

$21

$150

$93

$33

$101

$58

$11

Sources: Authors’ calculations from the Medical Expenditure Panel Survey 

2007 and MarketScan Outpatient Pharmaceutical Claims, 2007 

1Remaining proportion of NDCs were classified as “Other” 

Pharmacy Component (MEPS PC), 
Table 2. Outlier Editing Thresholds for Underreported Payments

Patent Status and Third Party Payer Amount Using RUP / AWUP Using RUP / median RUPMS

Generics 

Positive third party amount <.03 & price <$2 <.02 

Zero third party amount <.03 <.25 

Missing third party amount <.15 <.35 

Brand Name Originators 

Positive third party amount <.20 & price <$2 <.02 

Zero third party amount <.20 <.03 

Missing third party amount <.70 <.90 

Single Source Brand Name 

Positive third party amount <.65 & price <$10 <.02 

Zero third party amount <.65 <.80 

Missing third party amount <.75 <.90 

MS = MarketScan 

Table 3. Agreement Rates in Validation Data Using Pharmacy-Report Prices and After Editing 

Edited Using 

Pharmacy Reports RUP / AWUP Using RUP / median RUPMS

Exact 

Lin’s 

concordance Exact 

Lin’s 

concordance Exact 

Lin’s 

concordance 

Overall 42.2% .743 41.9%** .922** 42.0%** .953**† 

Patent Status 

Single Source Brand Name 37.0% .708 37.1% .900** 36.9%†† .941**† 

Brand Name Originators 60.3% .563 60.4% .950** 58.8%**†† .968** 

Generic 42.9% .579 42.3%** .877** 42.8%**†† .885**

MS = MarketScan 

Sources: Authors’ calculations from 2007 Medical Expenditure Panel Survey Pharmacy Component matched to Medicare 

claims files. Median RUPs calculated using MarketScan Outpatient Pharmaceutical Claims, 2007. N=15,375. 

Notes: AWUP = retail unit price /average wholesale unit price. RUP = retail unit price. 

** = statistically significantly different from unedited pharmacy reports at the .01 level. 

† = statistically significantly different from data edited using the AWUP at the .05 level. 

†† = statistically significantly different from data edited using the AWUP at the .01 level. 

Table 4. Mean Prices per Fill Under Two Sets of Editing Rules 

Edited Using Percent Difference 

RUP / AWUP Using RUP / median RUPMS (Median RUPMS/AWUP) 

Overall $66.67 $64.86†† −2.7% 

Patent Status 

Single Source Brand Name $150.71 $146.05†† −3.1% 

Brand Name Originators $63.19 $59.08†† −6.5% 

Generic $22.22 $22.23 +.0% 

Sources: Authors’ calculations from 2007 Medical Expenditure Panel Survey Pharmacy Component. Median RUPs 

calculated using MarketScan Outpatient 

MS = MarketScan 

Pharmaceutical Claims, 2007. N=192,304. 

†† = statistically significantly different from data edited using the AWUP at the .01 level. 
Figure 1. Density distributions of RUP / median RUPMS for single source drugs. 
Figure 2. Density distributions of RUP / median RUPMS for originator drugs. 
Figure 3. Density distributions of RUP / median RUPMS for generic drugs.