Exploring Potential Benefits of 

Enumerating All Prescribed Medicines 

as a Tool for Estimating Opioid Use in 

the Medicare Current Beneficiary 

Survey (MCBS)

November 4, 2021

Becky Reimer

Elise Comperchio

Andrea Mayfield

Jennifer Titus

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 2

Opioid use and misuse is a substantial problem 

among Medicare beneficiaries in the United States.

• Medicare beneficiaries face unique health challenges making 

them potentially more susceptible to opioid use and misuse, 

including multiple comorbidities, chronic-pain associated 

conditions, and mental and behavioral health issues (Niles, et 

al., 2020; Dean, 2017, Wright, et al., 2014). 

• Many of these conditions require complex drug therapy 

involving multiple prescriptions for long periods of time, which 

can increase the risk for opioid harm (Dean, 2017; 

Ramachandran, et al., 2021; Raman, et al., 2019). 
APPROACHES TO MEASURING OPIOID USE 3

Different methodologies yield inconsistent 

estimates and measurement gaps for segments of 

the Medicare population.

Survey-Reported 

Sources

Direct collection of opioid use and misuse

Items ask respondents about opioid use or 

misuse, using drug names and/or pictures to 

spur recall

Enumeration method

Enumeration of all prescription drugs; can be 

enhanced during data processing by matching 

to claims data and/or linking to an 

administrative list of opioids to identify opioid 

use

Administrative 

Data Sources

Provider sources

Pharmacy data

Claims

Medicare Part D Prescription Drug Claims
STRENGTHS AND LIMITATIONS OF MEASUREMENT APPROACHES 4

Administrative Data Sources Strengths Limitations

• IQVIA Total Patient Tracker

• Medicare Part D Claims 

Data

• Important to assessing 

opioid prescribing practices

• Do not provide data on 

medication adherence 

• Do not provide contextual 

health information, such as 

health status

• Potential for coverage bias 

since ~30% of beneficiaries 

are not covered by Medicare 

Part D

Survey-Reported Data Strengths Limitations

Direct collection of opioid use or 

misuse:

o National Health Interview 

Survey (NHIS)

o National Survey of Drug Use 

and Health (NSDUH)

• Provides valuable data on 

opioid use disorder

• Allows for collection of 

contextual health 

information 

• Recall bias due to selfreport and social 

desirability bias

• Do not provide data 

representative of the 

Medicare population

Enumeration-based approaches:

o Medicare Current 

Beneficiary Survey (MCBS)

o Medical Expenditure Panel 

Survey (MEPS)

• Recall bias can be mitigated 

in data collection and 

processing 

• MCBS provides 

representative data for 

Medicare population

• Recall bias due to selfreport
5

The Medicare Current Beneficiary Survey (MCBS) 

is a continuous, multi-purpose longitudinal survey.

• The MCBS represents the population of Medicare beneficiaries aged 65 and 

over and beneficiaries aged 64 and under with certain disabling conditions 

living in the United States. 

• The MCBS is sponsored by the Office of Enterprise Data and Analytics (OEDA) 

of the Centers for Medicare & Medicaid Services (CMS) and is conducted 

through a contract with NORC at the University of Chicago (NORC).

• The MCBS serves as the leading source of information on the Medicare 

program and its impact on beneficiaries, including health care utilization and 

costs.

WHAT IS THE MCBS?
HOW DOES THE MCBS MEASURE DRUG USE? 6

The MCBS employs an enumeration-based approach to 

collect prescription medication utilization. 

• Respondents are asked to report any medications filled in the 

reference period with the aid of available documentation, such as 

prescription drug labels.

• Data entry into the instrument is facilitated by a Prescription Medicine 

Lookup (PMLU) tool, which is powered by the First Databank (FDB) 

MedKnowledgeTM database of all available prescribed medicines. 

• Medicare Part D claims data are used to enhance self-reported data 

during data processing. 
RESEARCH QUESTIONS 7

1

Is it feasible to estimate opioid usage by enumerating all prescribed medicines during data 

collection and determining which, if any, are opioids in data processing?

2

What proportion of Medicare beneficiaries are estimated to have obtained at least one 

prescription opioid during a given year using this approach?

3

How do estimates based on this approach compare to those from external benchmarks?

4

How much does this estimate change once survey data are matched to Part D claims data 

(adding new medicines that were not reported in the survey, but that are present in claims)?

5

How do opioid use estimates vary based on beneficiary characteristics, including 

demographics and health factors?
Methods
METHODS 9

MCBS Prescription Medicine Data Life Cycle
METHODS 10

Building an Analytic Dataset
METHODS 11

Metrics:

• Beneficiary level:

• “Any opioid use”: at least one opioid reported in 2018

• “Consistent opioid use”: at least one opioid reported in each 2018 interview

• Total opioid counts:

• Survey-reported opioid count

• Survey-reported and claims-only opioid count

All analyses are unweighted, because:

• This is a methodological investigation.

• The MCBS does not create weights for the specific subset of beneficiaries 

included in this analysis (age 65+, completing >= 2 interviews in 2018).
Results
RESULTS 13

Estimates of Any Opioid Use Based on Beneficiary Age

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

24.6%

35.1%

22.6%

30.0%

41.0%

27.9%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

Overall Age <65 Age 65+

Survey-Reported Survey-Reported + Claims
RESULTS 14

Overall Opioid Counts

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

• For every 10 Medicare beneficiaries in this analysis:

• Three opioid medicines were reported in the survey:

• ...and a fourth opioid was identified in claims matching:
RESULTS 15

Proportion of Beneficiaries with Any Opioid Use and

Consistent Opioid Use

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

20.9%

9.1%

70.0%

Any opioid use

Consistent opioid use

No opioids reported
RESULTS 16

Benchmark Comparison 

This study estimates that 27.9% of beneficiaries age 65+ had any 

opioid use during the calendar year, which is comparable to external 

benchmarks.

Year(s) Data Source Methodology Findings Citation

2018 MCBS Survey-reported 

medicines + claims

27.9% N/A –

present study

2018 IQVIA Total 

Patient Tracker

Retail pharmacy data 25% CDC, 2019

2015-

2016

MEPS Survey-reported 

medicines, with 

additional data 

collection from 

pharmacies (pending 

respondent consent)

19.3% Moriya and 

Miller, 2018
RESULTS 17

Odds of Opioid Use, Based on Age Group

Models predicting any/consistent opioid included socio-demographic, 

health status, chronic condition, and chronic pain predictors.

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

0.42***

0.46***

0.55***

1.00

0.54***

0.68***

0.74*

1.00

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Age 85+

Age 75-84

Age 65-74

Age <65

Odds of Opioid Use

Any Opioid Use Consistent Opioid Use

+ p<0.5 * p<0.01 ** p<0.001 *** p<0.0001
RESULTS 18

Odds of Opioid Use, Based on Socio-Demographics

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

1.86***

1.32*

1.23+

1.49***

0.98

1.08

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Part D coverage

<=200% Federal

Poverty Level

Rural area

Odds of Opioid Use

Any Opioid Use Consistent Opioid Use

+ p<0.5 * p<0.01 ** p<0.001 *** p<0.0001
RESULTS 19

Odds of Opioid Use, Based on Health Characteristics

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

1.47*

1.96***

3.03***

1.71***

1.49***

1.61***

3.44***

1.40***

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

Has high-impact chronic

pain (limits life or work

activities most or every day)

Has chronic pain

(pain most or every day)

Has >= 9 non-opioid

prescription medicines

Self-reported

poor health

Odds of Opioid Use

Any Opioid Use Consistent Opioid Use

+ p<0.5 * p<0.01 ** p<0.001 *** p<0.0001
RESULTS 20

Odds of Opioid Use, Based on Chronic Conditions

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

0.74**

0.96

1.36*

1.62+

1.61***

1.62***

0.87+

1.16+

1.17+

1.00

1.23**

1.53***

0.0 0.5 1.0 1.5 2.0 2.5

Diabetes

Cancer

Depression

Dementia

Osteoporosis/

broken hip

Arthritis

Odds of Opioid Use

Any Opioid Use Consistent Opioid Use

+ p<0.5 * p<0.01 ** p<0.001 *** p<0.0001
Discussion
DISCUSSION 22

Enumeration of all prescribed medicines is a 

feasible approach for estimating opioid use among 

Medicare beneficiaries.

• After matching survey data to Part D claims data, the 

proportion of beneficiaries using any opioids rose from 

24.6% to 30.0% 

• Based on this analysis, 9.1% of beneficiaries were 

classified as consistent opioid users

• This study’s estimate for any opioid use among adults age 

65+ (27.9%) was comparable to external benchmarks

• Multivariate models to predict opioid use correctly

classified the majority of beneficiaries (73.2% for any use 

and 91.0% for consistent use)

• Both models had higher specificity than sensitivity, meaning they were 

better able to predict beneficiaries without opioid use outcomes
DISCUSSION 23

Some socio-demographic and health characteristics associated 

with any opioid use differ from those associated with consistent 

opioid use.

• Predictors of both any opioid 

use and consistent use

– Age <65

– White non-Hispanic (compared to 

Hispanic)

– less than high school degree or 

some college (compared to high 

school degree)

– Part D coverage

– >=9 prescriptions

– poor health

– chronic pain

– high-impact chronic pain

– having arthritis, osteoporosis, or 

depression, 

– not having diabetes

• Predictors of any opioid use 

(but not consistent use)

– Having cancer

– not having hypertension

• Predictors of consistent opioid 

use (but not any use)

– Black non-Hispanic (compared to 

White non-Hispanic)

– residence in rural area

– <= 200% of Federal Poverty Level

– having dementia

– not having high cholesterol, 

mental condition, or stroke
LIMITATIONS AND NEXT STEPS 24

Limitations:

• Recall bias due to self-report of opioid prescriptions

• Inability to match to claims data for beneficiaries not enrolled in 

Part D (~30% of beneficiaries*)

• Only collects data on prescribed opioid use and does not capture 

possible misuse

• Use of unweighted data limits generalizability of results

Looking ahead, we will continue to investigate:

• Are there cross-sectional trends in beneficiaries’ opioid use over 

time?

• Among beneficiaries who use opioids, can we estimate the 

quantities they obtain and their frequency of use?

*https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Information-on-Prescription-Drugs/MedicarePartD
PUBLIC HEALTH IMPLICATIONS 25

The Medicare population faces unique and complex 

challenges that may require a different approach to

addressing opioid misuse.

• Painkillers are the most commonly misused prescription among 

elderly (Dean, 2021)

• Misuse may be treated as uncommon or may be undiagnosed or 

misdiagnosed as other comorbidities

• Study contributions include:

• Estimating opioid use among beneficiaries without Part D coverage to 

mitigate potential for underestimation via other methodologies

• Distinguishing between “any opioid use” versus “consistent opioid use” in 

an effort to better identify the potential for misuse

• Identifying diverse list of socio-demographics and health conditions 

associated with increased risk of opioid misuse among the Medicare 

population
Thank you.

Becky Reimer

Senior Data Scientist

Reimer-Becky@norc.org

Elise Comperchio

Research Scientist

Comperchio-Elise@norc.org

Andrea Mayfield

Senior Research Director I

Mayfield-Andrea@norc.org

Jennifer Titus

Senior Research Director I

Titus-Jennifer@norc.org

Data Sources:

• MCBS Cost Supplement and Survey File LDS files

• Data available to the public as a free download here:

https://www.cms.gov/Research-Statistics-Data-andSystems/Downloadable-Public-Use-Files/MCBSPublic-Use-File/index
RESULTS 27

Multivariate Model Results: Any/Consistent Opioid Use

.

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

Any Opioid Use Consistent Opioid Use

(N=8,093)

Pseudo R2 = 0.20

Correct Classification = 73.2

Sensitivity = 31.1, Specificity = 91.2

(N=8,093)

Pseudo R2 = 0.22

Correct Classification = 91.0

Sensitivity = 7.7, Specificity = 99.3

Socio-demographic Variables OR 95% CI OR 95% CI

Age: (reference: <65 years) 

65-74 years 0.74* (0.62, 0.89) 0.55*** (0.43, 0.72)

75-84 years 0.68*** (0.57, 0.82) 0.46*** (0.35, 0.60)

85+ years 0.54*** (0.44, 0.67) 0.42*** (0.31, 0.58)

Sex: Female 0.96 (0.86, 1.08) 0.04 (0.23, 0.63)

Race/ethnicity (reference: White)

Hispanic 0.78+ (0.64, 0.95) 0.57* (0.41, 0.80)

Non-Hispanic Black 1.19 (0.99, 1.42) 1.49* (1.16, 1.90)

Other race 0.91 (0.71, 1.15) 1.31 (0.93, 1.84)

Educational attainment 

(reference: high school degree)

Less than high school degree 1.23+ (1.05, 1.45) 1.34+ (1.05, 1.70)

Some college/vocational 

school 1.17+ (1.02, 1.35) 1.39* (1.12, 1.72)

Bachelor’s degree or higher 1.01 (0.87, 1.19) 1.05 (0.80, 1.39)

Residence in rural area 1.08 (0.95, 1.22) 1.23+ (1.03, 1.48)

<=200% of the Federal Poverty 

Level 0.98 (0.86, 1.11) 1.32* (1.08, 1.61)

Part D coverage 1.49*** (1.28, 1.72) 1.86*** (1.41, 2.46)

+ p<0.5 * p<0.01 ** p<0.001 *** p<0.0001
RESULTS 28

Multivariate Model Results: Any/Consistent Opioid Use

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

+ p<0.5 * p<0.01 ** p<0.001 *** p<0.0001

Any Opioid Use Consistent Opioid Use

Health Variables OR 95% CI OR 95% CI

Has >=9 prescription 

medicines 3.44*** (3.06, 3.87) 3.03*** (2.48, 3.71)

Self-reported poor health 

status 1.40*** (1.22, 1.60) 1.71*** (1.41, 2.06)

Chronic conditions

Arthritis 1.53*** (1.37, 1.70) 1.62*** (1.36, 1.94)

Heart disease 0.93 (0.82, 1.04) 0.93 (0.77, 1.11)

Osteoporosis/broken hip 1.23* (1.08, 1.41) 1.61*** (1.33, 1.95)

Parkinson’s disease 1.12 (0.74, 1.69) 0.89 (0.47, 1.71)

Cancer 1.16+ (1.01, 1.32) 0.96 (0.78, 1.19)

Hypertension 0.87+ (0.77, 0.99) 1.04 (0.85, 1.28)

Diabetes 0.87+ (0.77, 0.98) 0.74** (0.61, 0.89)

High cholesterol 0.94 (0.84, 1.07) 0.79+ (0.66, 0.94)

Alzheimer’s disease 1.13 (0.81, 1.59) 1.09 (0.64, 1.86)

Dementia, other than 

Alzheimer’s 1.00 (0.73, 1.35) 1.62+ (1.07, 2.46)

Depression 1.17+ (1.02, 1.33) 1.36* (1.12, 1.65)

Mental condition 0.89 (0.73, 1.08) 0.70+ (0.53, 0.92)

Stroke 0.91 (0.77, 1.07) 0.70* (0.54, 0.91)

Pulmonary disease 1.11 (0.98, 1.27) 1.10 (0.91, 1.33)

Chronic pain 1.61*** (1.40, 1.85) 1.96*** (1.58, 2.42)

High-impact chronic pain 1.49*** (1.22, 1.82) 1.47* (1.14, 1.91)
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