Using Medicare Part D Data

(1)

Using Medicare Part D Data

Holly M. Holmes, MD

(2)

Objectives

•

Understand the contents of the Medicare Part

D ﬁles

•

Discuss the strengths and limitations in using

(3)

Medicare Part D – what is it?

•

Established by the Medicare Prescription

Drug, Improvement, and Modernization Act

of 2003

•

Covers prescription medications

–

Not OTC, not supplements

•

Available to all 43 million Medicare

beneﬁciaries

(4)

PART D QUIZ

True or False:

Enrollment in Part D is optional.

True or False:

Once you choose a Part D plan, you cannot

switch.

(5)

Part D enrollment

•

60% of Medicare beneﬁciaries were enrolled

in Part D in 2011

•

Open enrollment for 2012: 10/15/11-‐12/7/11

•

6% switch every year (therefore 94% do not

(6)

PART D QUIZ

True or False:

Medicare Advantage enrollees who have Part D

coverage are enrolled in stand-‐alone Part D

(7)

Part D Enrollment in 2010

(8)

Part D Enrollment in 2010

Source: ResDAC

If you are studying A+B+D, you really

only have 37% of Medicare

beneﬁciaries.

And since ½ of PDP is Low Income

Subsidy, you only have 18% of

“middle-‐class”-‐ish benes.

If you want to study all of the Low

Income Subsidy beneﬁciaries, you

need both PDP and MAPD.

(9)

PART D QUIZ

Part D plans can oﬀer diﬀerent

A.

Deductibles

B.

Copays

C.

Premiums

D.

Gap Coverage

(10)

This goes to 0 by 2020 (fed govt picks up rest)

Source: Kaiser Family Foundation.

These are determined based on true out-‐of-‐ pocket cost (TROOP)

(11)

This goes to 0 by 2020 (fed govt picks up rest)

Source: Kaiser Family Foundation.

These are determined based on true out-‐of-‐ pocket cost (TROOP)

Beneﬁciary Phase Variable

identiﬁes where the bene

is when the prescription is

ﬁlled.

(12)

Part D QUIZ

True or False:

Part D data for Medicare Advantage (HMO)

beneﬁciaries is available to researchers.

(13)

What types of Part D data are

available?

•

Public use (landscape) ﬁles

–

Not linkable to beneﬁciary-‐level ﬁles

•

Beneﬁciary-‐Level Part D Data

–

Final Part D Rule (5/28/2008): keeps sensitive data

encrypted and cost data aggregated

(14)

What types of Part D data are

available?

•

Public use (landscape) ﬁles

–

Not linkable to beneﬁciary-‐level ﬁles

•

Beneﬁciary-‐Level Part D Data

–

Final Part D Rule (5/28/2008): keeps sensitive data

encrypted and cost data aggregated

–

Cannot identify prescriber, pharmacy, or plan

Part D ﬁles are linkable to A + B only

through the beneﬁciary ID, which is

encrypted and the same as the bene

ID from A + B. This will not change

until the Part D Rule changes.

(15)

Part D Denominator File

•

Called the Master Beneﬁciary Summary File

(MBSF)

•

Contents

–

Beneﬁciary Summary File (BSF)

–

Chronic Conditions (26)

–

Cost & Utilization – annual summary

(16)

Part D Denominator File

•

Called the Master Beneﬁciary Summary File

(MBSF)

•

Contents

–

Beneﬁciary Summary File (BSF)

–

Chronic Conditions (26)

–

Cost & Utilization – annual summary

–

Death Information

The BSF is available by summer of year

20xx+1.

The Part D denominator part of the

BSF is added in March of 20xx+2.

You may need 2 pieces of the BSF.

Demographic information known to

CMS overwrites all demographic

information from a claim or Part D event

in the MBSF.

That means agreement between MBSF

and claims/PDE unless there is a change.

(17)

Part D Event Files

•

Bene_ID

•

Drug info: from NDC linkage with First Data Bank

–

Generic/brand name

–

Dosage (strength and unit of measure, eg mg)

–

Form (tablet, capsule, cream, spray, patch)

•

Claim info:

–

Quantity dispensed

–

Date dispensed

–

Days supply

(18)

Part D Event Files

•

Bene_ID

•

Drug info: from NDC linkage with First Data Bank

–

Generic/brand name

–

Dosage (strength and unit of measure, eg mg)

–

Form (tablet, capsule, cream, spray, patch)

•

Claim info:

–

Quantity dispensed

–

Date dispensed

–

Days supply

The directions on the prescription are

not part of the PDE ﬁles. Frequency of

dosing can be calculated by quantity

dispensed / days supply.

(19)

Dosing Frequency Example

Drug

% of Claims with Dosing Frequency

<1/day

1-‐<2/day

2-‐<3/day

>3/day

Amlodipine

6.4

89.3

4.1

0.22

(20)

Part D Appended Files

•

Prescriber

–

Encrypted ID, type, specialty, subspecialty, and state

•

Plan

–

Encrypted ID, beneﬁts, premiums, tiers, service areas

•

Pharmacy

(21)

Part D Appended Files

•

Prescriber

–

Encrypted ID, type, specialty, subspecialty, and state

•

Plan

–

Encrypted ID, beneﬁts, premiums, tiers, service areas

•

Pharmacy

–

Encrypted ID, type of pharmacy, state

The encrypted ID for prescribers,

plans, and pharmacies does not

link with A or B ﬁles or with any

other beneﬁciary-‐level ﬁles.

(22)

PART D QUIZ

True or False:

Medicare beneﬁciaries with Medicaid (dual

eligibles) continue to get their prescriptions

through Medicaid.

(23)

(24)

Identifying Low Income

Beneﬁciaries

•

State Buy-‐In: A state paid for the beneﬁciary’s Part B

coverage through Medicaid or a savings program

•

Low Income Subsidy: benes with help paying

premiums, deductibles, and/or copay, no gap, no late

enrollment

•

Dual Eligible status: traditional Medicaid and other

(25)

Identifying Low Income

Beneﬁciaries

•

State Buy-‐In: A state paid for the beneﬁciary’s Part B

coverage through Medicaid or a savings program

•

Low Income Subsidy: benes with help paying

premiums, deductibles, and/or copay, no gap, no late

enrollment

•

Dual Eligible status: traditional Medicaid and other

Medicare savings programs

How to identify:

State Buy-‐In

: State buy-‐in variable in BSF

LIS

: Cost share group variable

Dual Eligible

: State reported dual eligible

(26)

PART D QUIZ

What is the minimum number of Part D plans a

beneﬁciary could choose from for 2012?

A.

5

B.

15

C.

25

(27)

What drugs are covered?

•

No OTCs

•

Protected Drugs:

–

Immunosuppressant

–

Antidepressant

–

Antipsychotic

–

Anticonvulsant

–

Antiretroviral

–

Antineoplastic

(28)

What drugs are covered?

•

No OTCs

•

Protected Drugs:

–

Immunosuppressant

–

Antidepressant

–

Antipsychotic

–

Anticonvulsant

–

Antiretroviral

–

Antineoplastic

Benzodiazepines and

barbiturates are not covered

under Medicare Part D, but they

may be covered by some states’

LIS programs

(29)

(30)

(31)

(32)

Medication-‐Centered Research

Ideas with Part D

• 

Polypharmacy

–

Overutilization

–

Use of inappropriate medications

• 

Suboptimal prescribing

–

Drug interactions

–

Underutilization

• 

Adherence

(33)

Medication Adherence

1.

Determine predictors of adherence in

Medicare Part D beneﬁciaries with

hypertension.

2.

Quan<fy the extent to which con<nuity of

care with a provider aﬀects adherence.

3.

Es<mate the amount of nonadherence

(34)

Methods

Study Popula<on

• 

Medicare claims and Part D event ﬁles for 5% sample of

Medicare beneﬁciaries

• 

66 and older on January 1, 2007

• 

Coverage

–

24 months A and B without HMO 2006-‐2007

–

12 months Part D in 2007

(35)

Methods

•

Stable an<hypertensive users

–

Uncomplicated Hypertension (401.xx)

–

PDE ﬁles for an<hypertensive medica<on in 2006 and 2007

–

Mul<ple med classes, categorized as BP meds by JNC7

–

No dose changes

(36)

Medication Possession Ratio: (MPR)

# days supply dispensed between 1st_{and last fill date}

# days between 1st_{and last fill date}

Exclude days supply on last fill date

Measure of adherence: MPR

Medica'ons

Thiazides Beta blockers

Calcium channel blockers ACEIs/ARBs

Vasodilators Clonidine Renin inhibitors

Alpha blockers

Potassium sparing diure<cs Loop diure<cs

(37)

Measures

•

Medica<on Possession Ra<o (MPR)

–  Sum of days supply between 1st and last ﬁll / total days between 1st and last ﬁll

•

Main outcome = % adherent

–  Adherent = average MPR 80% or greater

•

Possible predictors

–  Demographics

–  Socioeconomic status

–  Comorbidity

–  Medica<on use

(38)

Age 66 on 1/1/07 n=382939

24 months of Part A and B n=319359

Part D events for blood pressure medica<ons

n=276443

Uncomplicated HTN n=236158

Medicare beneﬁciaries with HTN n=471190 1,698684 benes without hypertension 88,251 younger than 66 on 1/1/07 63,580 with HMO 14,900 no BP meds 27,810 with <2 claims 206 with dose change

Medicare enrollees with PDE ﬁles in 2006 – 2007 n=2,169,874 38,125 comp. HTN 2160 with MPR >1.43 Uncomplicated HTN N=168522 Study Popula<on N=168522 56,651 hospitalized 2007 9,550 in nursing home 324 in Territories or unknown

(39)

Table 1. Characteristics of Study Population

(n=168,522)

Variable Category Total

(Column %) Percent Adherent

AGE GROUP 66-‐69 36468 (21.6%) 78.9% 70-‐74 42178 (25%) 79.3% 75-‐79 37936 (22.5%) 79.7% 80-‐84 28488 (16.9%) 79.2% 85+ 23452 (13.9%) 80.5% SEX Female 116942 (69.4%) 79.4% Male 51580 (30.6%) 79.6% RACE/ETHNICITY Non-‐Hispanic white 137981 (81.9%) 81.5% Black 14249 (8.5%) 67.8% Hispanic 9656 (5.7%) 69.3% Amer. Indian/Alaskan 563 (0.3%) 67.7% Asian/paciﬁc island 4950 (2.9%) 78.4% Other 882 (0.5%) 74.7% Unknown 241 (0.1%) 80.1%

(40)

Table 1. Characteris'cs of Study Popula'on (n=168,522)

DIVISION

East North Central 27970 (16.6%) 82%

East South Central 13688 (8.1%) 77.3%

Middle Atlantic 20407 (12.1%) 80.4%

Mountain 7189 (4.3%) 78.2%

New England 10079 (6%) 83%

Paciﬁc 17964 (10.7%) 76.7%

South Atlantic 35746 (21.2%) 78%

West North Central 17192 (10.2%) 84.2%

West South Central 18287 (10.9%) 75.6%

LOW-‐INCOME SUBSIDY No 127781 (75.8%) 80.5% Yes 40741 (24.2%) 76.1% MEDIAN INCOME IN CENSUS TRACT 0 -‐ 31,000 36229 (22.1%) 76.1% 31,000-‐38,000 42412 (25.9%) 79.8% 38,000-‐49,000 42033 (25.7%) 80.6% 49,000+ 43187 (26.4%) 81% % IN CENSUS TRACT WITH <12 YEARS EDUCATION 0 -‐ 12.2 41473 (25.3%) 81.7% 12.2-‐18.6 40877 (24.9%) 81.6% 18.6-‐27.1 40786 (24.9%) 79.5% 27.1+ 40712 (24.8%) 75.2%

(41)

Table 1. Characteristics of Study Population (n=168,522)

DEPRESSION No 155027 (92%) 79.6% Yes 13495 (8%) 78.2% DEMENTIA No 160071 (95%) 79.5% Yes 8451 (5%) 79% NUMBER OF ELIXHAUSER S CONDITIONS 0-‐1 comorbidity 74943 (44.5%) 80.4% 2-‐3 comorbidity 62119 (36.9%) 79.3% 4+ comorbidity 31460 (18.7%) 77.6% IN COVERAGE GAP IN 2007 No_Yes 108046 (64.1%)_{60476 (35.9%)} _83.5%77.2%

NUMBER OF MEDS 8.9 (+/-‐4.8)

(42)

Logistic Regression Model for Adherence

(n=168,522)

Characteristic Odds Ratio (95% CI)

Low Income Subsidy 1.14 (1.10-‐1.18)

% in Census Tract <12 yrs Education (>18.6% vs. 0-‐18.6%)

0.93 (0.90-‐0.95)

Depression 0.94 (0.90-‐0.99)

Comorbidity (0-‐1 conditions as reference)

2 to 3 conditions 0.93 (0.90-‐0.95)

4 or more conditions 0.85 (0.82-‐0.88)

Number of Medications 0.97 (0.97-‐0.98)

In the Coverage Gap in 2007 1.65 (1.60-‐1.70)

Total Copay ($1000 incr.) 1.23 (1.20-‐1.25)

(43)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1 8 ₁₅ ₂₂ ₂₉ ₃₆ ₄₃ ₅₀ ₅₇ ₆₄ ₇₁ ₇₈ ₈₅ ₉₂ ₉₉ 10 6 11 3 12 0 12 7 13 4 14 1 14 8 155 162 169 176 183 190 197 20 4 21 1 21 8 22 5 23 2 23 9 246 253 26 0 26 7 27 4 281 288 295 302 Percent of Population in HRR with ≥80% Adherence

Regional Diﬀerences in Adherence

% ADHERENT

0.757

0.793

0.836

(44)

Regional Diﬀerences

•

Model with HRR (mull): ICC = 1.8%

•

Model with HRR level variables: ICC = 1.4%

–

Medicare enrollees in HRR

–

Total Part B expenditure

–

Primary Care Physicians in HRR

•

Model with Pa<ent level variables: ICC = 1.0%

(45)

Conclusions

•

Signiﬁcant diﬀerences in an<hypertensive

medica<on adherence among diﬀerent racial/

ethnic groups and in persons with higher

levels of comorbidity.

•

Diﬀerences by region, medica<on use, number

(46)

(47)

(48)

(49)

Medication-‐Centered Research

Ideas with Part D

• 

Polypharmacy

–

Overutilization

–

Use of inappropriate medications

• 

Suboptimal prescribing

–

Drug interactions

–

Underutilization

• 

Adherence

(50)

Inappropriate Medication Use

1. 

Investigate the utility of Medicare Part D data to

describe prescriber-level variation in medication use

2. 

Evaluate the variation in PIM use in Medicare Part D

beneficiaries at the prescriber level, controlling for

patient characteristics associated with getting a PIM

(51)

Design and Methods

•

100% Texas Medicare claims and Part D event files for 2007

and 2008

•

Enrollees 66 and older in 2008 with 12 months of A, B, and D,

without HMO in 2008

•

Prescribers who were physicians, with 10 or more beneficiaries

per prescriber

•

PIMs defined according to Beers 2003 list

•

List of medications/drug classes only (did not include

drug-disease combinations)

(52)

Design and Methods

Variables Data Source

Patient Age, sex, race/ethnicity, state buy-‐in PDE denominator

Comorbidities (Elixhauser s Index) 2007 carrier ﬁle and MEDPAR

Hospitalization in 2007 MEDPAR

PIM in 2008 according to Beers list PDE ﬁles

Prescriber Credentials, specialty PDE Prescriber Characteristics

File

Analysis Plan

Patient and prescriber characteristics associated with PIM use by

patients

•

Bivariate

•

Multivariable model for patient factors

•

Multilevel model for prescriber, controlling for patient level

(53)

Texas Medicare Part D

Beneﬁciaries Age 66 in 2008

N = 2,261,766

12 months of A, B, and D coverage

and no HMO all of 2008

N = 760,703

Had Part D claims for any drug in

2008

N = 716,930

10 or more beneﬁciaries per

physician prescriber

N = 677580 (24,561 MD/DO)

(54)

Results: Number of Beneficiaries Per

Prescriber

Distribution of BID_PCT_PROVIDER & CLM_PCT_PROVIDER

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 0 1 2 3 4 5 6 7 8 9 Pe rc en t BID_PCT_PROVIDER

Number of beneﬁciaries per prescriber

Pe

rc

en

t o

f p

re

sc

rib

er

s

(55)

Results: Prevalence of PIM Use

•

Overall, 216,364 (31.9%) of 677,580

Texas Part D beneficiaries who filled

prescriptions received a PIM in 2008

•

85% of the 24,561 prescribers prescribed

(56)

Table 1: Characteristics of 677,580 Beneficiaries

•

PIM use increased with increasing age, and differed

between sexes and categories of race/ethnicity

Characteristic Category Number % Getting a PIM

Age 66-‐69 157,530 29.6 70-‐74 171,984 30.9 75-‐79 142,225 32.7 80-‐84 107,999 33.8 85+ 97,842 34.4 Sex Female 441,657 35.0 Male 235,923 26.2 Race/Ethnicity White 465,680 32.2 Black 52,611 34.2 Hispanic 139,223 31.3 Asian 16,797 22.9 Other 3,269 28.3 PIM – poten<ally inappropriate medica<on

(57)

Table 1 (cont’d): Characteristics of 677,580 Beneficiaries

Characteristic Number % Getting a PIM

State Buy-‐in in 2008 YES 206,113 35.0

NO 471,467 30.6

Hospitalization in 2007 143,741 41.5

Comorbidities Heart Failure 108,703 42.5

Uncomplicated DM 213,993 35.9 Complicated DM 81,271 40.3 Hypertension 523,380 34.2 Pulmonary Disease 150,589 38.5 PVD 133,021 39.7 Depression 70,283 42.4 Cancer 72,654 32.7 Psychoses 43,211 40.1 Neurologic Disorder 91,586 37.7

Total Number of Medication Claims

(SD) 39.2 (+/-‐ 32.0) 52.2 (+/-‐ 35.9) PIM – poten<ally inappropriate medica<on DM – Diabetes mellitus PVD – peripheral vascular disease

(58)

Table 2: PIM Use According to Number of

Prescribers

•

73% of beneficiaries had >1 prescriber for all prescriptions

•

PIM use increased considerably with increased numbers of

unique prescribers

Number of Unique

Prescribers Beneﬁciaries Number of % of Beneﬁciaries Getting a PIM

1 182,884 19.2

2 178,487 26.6

3 130,779 34.1

(59)

Table 3: Prescriber Characteristics and PIM Use

• 

14.4% of all beneficiaries who got a prescription

from an MD got a PIM from that MD

Characteristic of Prescriber

Number of

Prescriptions

% of Beneﬁciaries

Getting PIMs

Credentials MD

1,753,953

14.4

DO

133,790

18.5

Specialty

Gen. Internal

Medicine

355,262

19.0

Family Medicine

438,185

19.3

General Practice

20,730

19.7

Internal Medicine

Specialty

364,597

8.0

Geriatrics 30,767 18.7

Gynecology

27,021

11.4

(60)

Table 4: 10 Most Commonly Prescribed PIMs

PIM Name

Beneﬁciaries

Number of

Propoxyphene

83,415

Nitrofurantoin

37,908

Clonidine

28,496

Cyclobenzaprine

27,893

Amitriptyline

19,390

Doxazosin

11,941

Amiodarone

10,906

Dicyclomine

9753

Carisoprodol

8475

Methocarbamol

7958

(61)

Table 5: Multivariable Model for Odds of PIM Use

Characteristic Odds Ratio 95% CI

Age 66-‐69 1.0 Ref 70-‐74 1.0 0.98-‐1.01 75-‐79 0.99 0.97-‐1.01 80-‐84 0.98 0.96-‐1.00 85+ 0.97 0.95-‐0.99 Gender Female 1.37 1.35-‐1.38 Male 1.0 Ref

State Buy-‐in Yes 1.11 1.09-‐1.12

No 1.0 Ref

Race/Ethnicity White 1.0 Ref

Black 1.04 1.02-‐1.07

Hispanic 0.94 0.92-‐0.95

Asian 0.74 0.71-‐0.77

(62)

Table 5 (cont’d): Multivariable Model for Odds of PIM Use

•

Adjusted for other patient factors, sex and hospitalization were still

significant, and most comorbidities were not statistically or clinically

significant predictors of getting a PIM.

Characteristic Odds Ratio 95% CI

Hospitalization in 2007 1.11 1.10-‐1.13 Heart Failure 0.98 0.96-‐0.99 Uncomplicated DM 0.92 0.91-‐0.94 Complicated DM 0.96 0.94-‐0.98 Hypertension 0.93 0.92-‐0.95 Pulmonary Disease 1.03 1.02-‐1.05 PVD 1.05 1.03-‐1.06 Depression 1.08 1.06-‐1.10 Cancer 0.97 0.95-‐0.99 Psychoses 0.84 0.82-‐0.86 Neurologic Disorder 0.85 0.84-‐0.87

(63)

Table 5 (cont’d): Multivariable Model for Odds of PIM Use

•

Increasing number of unique prescribers remained a strong

independent predictor of PIM use in the multivariable model for

patient factors.

Number of Unique

Prescribers Odds Ratio 95% CI

1 1.0 Ref

2 1.42 1.40-‐1.44

3 1.90 1.87-‐1.94

(64)

!

Results: Adjusted % of Beneficiaries on PIMs Across All Prescribers

Ad ju st ed % o f b en eﬁ ci ar ie s ge in g a PIM Prescribers (N=10747) 1113 (10.4%) 607 (5.7%) 10th_{percen'le = 13.4%} 90th_{percen'le = 30.0%} mean = 21.1%

(65)

Opportunities Provided by Part D Data

•

Link with other data sources

–

MCBS data

–

SEER, Texas Cancer Registry

– 

Medicare A and B

(66)

Using MAPD Data

•

Risk adjustment

is still possible

with Rx-‐Risk

(67)

What can you do with Part D Data?

•

Toxicities

•

Cost

•

Policy

•

Access

•

Disparities

(68)

Questions?

[email protected]

Using Medicare Part D Data