Improving self-reported prescription medicine 

data quality with a commercial database 

lookup tool and claims matching

11.04.21 

Kali Defever

Becky Reimer

Michael Trierweiler

Elise Comperchio

The opinions and views expressed in this work are 

those of the authors. No official endorsement by the 

Department of Health and Human Services or the 

Centers for Medicare & Medicaid Services is intended 

or should be inferred.
INTRODUCTION: BACKGROUND 2

Background

– Regular prescription medicine use is a fact of life for the majority of older 

Americans (Mitchell et al., 2005) 

• Accurate measurement across the population is essential to inform healthcare 

policy

• Common measurement methods have contrasting strengths and weaknesses:

– This study assesses how using a combination of these sources can improve 

the quality of reporting and reduce measurement error

Method / Data Source Strengths Weaknesses

Administrative claims data High quality data Coverage bias – excludes 

those without coverage

Claims data are not timely

Representative surveys Can cover wider portion of 

the population of interest

Can include several 

sources of error and may 

contain gaps in information 

(Mitchell et al., 2005, Zhang 

and Koru, 2020, Gnijdic et 

al., 2017)
INTRODUCTION: RESEARCH QUESTIONS 3

Research Questions

1. Can a questionnaire lookup tool powered by a commercial 

database increase the quality of self-reported prescription 

medicine data?

2. How can Medicare Part D claims matching in post-processing 

improve survey reported data? What does this reveal above 

coverage gaps?

3. What do improvements from the lookup tool and claims 

matching reveal about differential measurement bias based 

on socio-demographics and health characteristics?

4. Is a survey approach equally effective for different types of 

medicines, such as widely prescribed vs. less common 

medicines?
4

Fall 

2017

Winter 

2018

Summer 

2018

Fall 

2018

Winter 

2019

INTRODUCTION: MCBS

What is the MCBS?

Medicare Current Beneficiary Survey

• The leading source of information on the Medicare program and its 

impact on beneficiaries, including health care utilization and costs

• Continuous, multipurpose, longitudinal survey that is conducted by the 

Centers for Medicare & Medicaid Services (CMS) through a contract with 

NORC at the University of Chicago

• Nationally representative sample of the Medicare population

• Round-based design

– 3 interviews per year

– 5 rounds per data year

2018 Interviews

2018 Data Year
INTRODUCTION: MCBS 5

Prescription Medicine Data Collection and Processing

• The MCBS employs an enumeration-based approach to 

collecting prescription medicines

– Respondents report medicine details for any prescriptions filled in a 

certain time period with the aid of available documentation, such as 

prescription drug labels

• Prescription Medicine Lookup tool (PMLU)

– In 2017, NORC replaced the existing lookup tool with a dynamic, 

user-friendly tool incorporating a commercial database 

– The PMLU is powered by the First Databank (FDB) MedKnowledgeTM

database (FDB, 2017) of prescribed medicines and is integrated into 

the Computer-Assisted Personal Interviewing (CAPI) software

• Claims matching 

– CMS matches survey-reported medicines to Medicare Part D claims 

to enhance self-reported data

– Claims are only available for beneficiaries enrolled in Part D
Methods
METHODS 7

MCBS Prescription Medicine Data Life Cycle
METHODS 8

Prescription Medicine Data Collection via PMLU Tool

Since [last interview date], have you had any prescriptions filled?

[If yes] What is the name of the medicine?

The tool presents potential medicines as interviewers begin typing:
METHODS 9

Prescription Medicine Data Collection via PMLU Tool

Once a medicine has been selected, the tool presents corresponding 

form and strength options:
METHODS 10

Claims Matching Process and Outcomes

Final MCBS medicine data include an indicator of data source, with 

three potential outcomes from the claims matching process:
METHODS 11

Building an Analytic Dataset
METHODS 12

Analytic Metrics

Impact of PMLU tool:

• Proportion of reported medicines for which interviewers locate a matching 

medicine within the tool

• This is an indicator of high data quality, because complete details are 

needed to select a medicine within the tool.

Impact of claims matching:

• Proportion of survey-reported medicines that were successfully matched to 

Part D claims

• This is an indicator of high data quality and accurate recall.

• Proportion of medicines in the final data that were not reported in the survey 

and were discovered in claims data

• This helps us understand differential underreporting.
Results
RESULTS: RESEARCH QUESTION 1 14

How has the addition of the PMLU impacted the 

quality of medicine data collected in the MCBS?

The proportion of 

prescription medicines

with database matches 

increased from 

2016 to 2018.

46%

56%

80%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2016 2017 2018

Proportion of survey-reported medicines

Proportion of survey-reported medicines with database 

matches by year

SOURCE: Centers for Medicare & Medicaid Services, Medicare Current Beneficiary Survey
RESULTS: RESEARCH QUESTION 1 15

How has the addition of the PMLU impacted the 

quality of medicine data collected in the MCBS?

Match rate was consistently higher for low utilizers with fewer medicines 

overall than high utilizers from 2016 to 2018, but the difference in match 

rate has narrowed over the years.

SOURCE: Centers for Medicare & Medicaid Services, Medicare Current Beneficiary Survey

54% 62%

84%

40%

54%

79%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2016 2017 2018

Proportion of survey-reported medicines

Proportion of survey-reported medicines found 

in PMLU by utilization

Low Utilizers (≤9 medicines) High Utilizers (>9 medicines)
RESULTS: RESEARCH QUESTION 2 16

How can Part D claims matching in post-processing 

improve survey reported data? 

No Part D, 

20%

Part D, 

80%

Claims are only available for 

beneficiaries enrolled in Part D

12%

100%

67% 20%

Part D

No Part D

0 10,000 20,000 30,000 40,000 50,000 60,000 70,000

Medicine Reporting Sources

Survey only Survey/Claims Claims only

SOURCE: Centers for Medicare & Medicaid Services, Medicare Current Beneficiary Survey 2018
RESULTS: RESEARCH QUESTION 2 17

Overall Increase in Medicines

SOURCE: Centers for Medicare & Medicaid Services, Medicare Current Beneficiary Survey 2018

• For every 10 Medicare beneficiaries in this analysis, 

eight have Part D coverage and two do not:

• The typical beneficiary in each group reported:

• ...and more medicines were identified in claims matching 

for those with Part D coverage:

0

D D D D D D D D
RESULTS: RESEARCH QUESTION 2 18

How can Part D claims matching in post-processing 

improve survey reported data? 

• 85% of survey-reported medicines have claims matches, which can validate and 

enhance medicine details and cost information1

For survey-reported medicines across data 

collection rounds1:

• 71% are consistently found in claims

• 13% are sometimes found in claims, but 

sometimes only reported in the survey in a 

particular round

• 16% are not found in claims

SOURCE: Centers for Medicare & Medicaid Services, Medicare Current Beneficiary Survey 2018

15%

85%

Not found in claims Found in claims 1 Limited to beneficiaries with Part D coverage
RESULTS: RESEARCH QUESTION 2 19

How can Part D claims matching in post-processing 

improve survey reported data? 

• Overall, the number of medicines increased by 21% after matching.

• Of all medicines in the final data, 17% were discovered through claims matching.

• To better understand differential likelihood of survey underreporting of medicine 

use, we ran a negative binomial model

• Factors associated with significantly more medicines discovered in claims 

matching: 

• Having a higher count of reported medicines

• Race/ethnicity

• Dual eligibility

• Having 4 or more chronic conditions 

• Having an income <200% of the poverty line

SOURCE: Centers for Medicare & Medicaid Services, Medicare Current Beneficiary Survey 2018
RESULTS: RESEARCH QUESTION 3 20

Model Results: Medicines Discovered in Claims

.

SOURCE: Centers for Medicare & Medicaid Services, Medicare Current Beneficiary Survey 2018

Number of Medicines Discovered 

in Claims Data

(N=7,622)

Socio-demographic Variables Estimate Standard Error

Age: (reference: <65 years) 

65-74 years -0.12+ 0.07

75-84 years -0.02 0.06

85+ years -0.02 0.07

Sex: Female 0.08+ 0.04

Race/ethnicity (reference: White)

Hispanic 0.25** 0.07

Non-Hispanic Black 0.19* 0.07

Other race/ethnicity -0.02 0.09

Educational attainment (reference: high school degree)

Less than high school degree 0.07+ 0.06

Some college/vocational school -0.04+ 0.05

Bachelor’s degree or higher -0.01 0.06

>=200% of the Federal Poverty Level -0.14* 0.05

Dual eligibility 0.57*** 0.06

Survey-reported medicine count 0.03*** 0.01

Has >= 4 chronic conditions 0.34*** 0.04

+ p<0.5 * p<0.01 ** p<0.001 *** p<0.0001
RESULTS: RESEARCH QUESTION 4 21

Is a survey approach equally effective for 

different types of medicines?

• Widely prescribed medicines are generally associated with higher data quality.

1 Limited to beneficiaries with Part D coverage

SOURCE: Centers for Medicare & Medicaid Services, Medicare Current Beneficiary Survey 2018

91%***

83%

Widely

prescribed

Less widely

prescribed

Survey-reported 

medicines found 

in claims1

99%***

86%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Widely

prescribed

Less widely

prescribed

Medicines found 

in PMLU 

database

10%

24%***

Widely

prescribed

Less widely

prescribed

Medicines 

discovered 

in claims1

+ p<0.5 * p<0.01 ** p<0.001 *** p<0.0001
Discussion
DISCUSSION 23

Discussion

• Implementing the PMLU tool improved the quality of medicine reporting.

• The tool supported respondent recall and limited potential sources of error.

• The increase in accuracy among beneficiaries with many medicines is particularly 

important.

• Claims matching mitigates recall problems and increases medicine estimates.

• This is particularly helpful to fill in gaps for beneficiaries with multiple chronic conditions 

and prescribed medicines.

• Combining survey and claims data can provide a more comprehensive view of 

prescription medicine use within the MCBS population.

• Coverage gaps still exist for beneficiaries who are not enrolled in Part D.

• Data quality can also vary based on medicine characteristics.

• More widely prescribed medicines were more likely to be found in the PMLU, and claims 

matching helped fill in gaps for less widely prescribed medicines.
Thank you.

Kali Defever

Data Analyst

Defever-kali@norc.org

Becky Reimer

Senior Data Scientist

Reimer-becky@norc.org

Michael Trierweiler

Senior Statistician

Trierweiler-michael@norc.org

Elise Comperchio

Research Scientist

Comperchio-elise@norc.org
CITATIONS 25

• First Databank, Inc. (2017). FDB MedKnowledgeTM. [database online]. 

Available from: http://www.fdbhealth.com/fdb-medknowledge/

• Gnjidic, D., Du, W., Pearson, S. A., Hilmer, S., & Banks, E. (2017). 

Ascertainment of self-reported prescription medication use compared 

with pharmaceutical claims data.

• Mitchell, A. A., Kaufman, D. W., & Rosenberg, L. (2005). Patterns of 

medication use in the United States. Annual Report of the Slone 

Epidemiology Center at Boston University (www.bu.edu/ 

slone/SloneSurvey/AnnualRpt/SloneSurvey-WebReport 2005.pdf).

• Zhang, Y., & Koru, G. (2020). Understanding and detecting defects in 

healthcare administration data: Toward higher data quality to better 

support healthcare operations and decisions. Journal of the American 

Medical Informatics Association, 27(3), 386-395.