Using Medicare Part D Data

Transcription

1 Using Medicare Part D Data Holly M. Holmes, MD Department of General Internal Medicine

2 Objectives Understand the contents of the Medicare Part D files Discuss the strengths and limitations in using Medicare Part D data in research

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 Started January 1, 2006

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 switch)

6 PART D QUIZ True or False: Medicare Advantage enrollees who have Part D coverage are enrolled in stand- alone Part D plans (PDP).

7 Part D Enrollment in 2010 Source: ResDAC

8 Part D Enrollment in 2010 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. Source: ResDAC

9 PART D QUIZ Part D plans can offer different A. Deductibles B. Copays C. Premiums D. Gap Coverage E. All of the above

10 These are determined based on true out- of- pocket cost (TROOP) This goes to 0 by 2020 (fed govt picks up rest) Source: Kaiser Family Foundation.

11 These are determined based on true out- of- pocket cost (TROOP) Beneficiary Phase Variable identifies where the bene is when the prescription is filled. This goes to 0 by 2020 (fed govt picks up rest) Source: Kaiser Family Foundation.

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 Cannot identify prescriber, pharmacy, or plan

14 What types of Part D data are available? Public use (landscape) files Not linkable to beneficiary- level files Beneficiary- Level Part D Data 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. Final Part D Rule (5/28/2008): keeps sensitive data encrypted and cost data aggregated Cannot identify prescriber, pharmacy, or plan

15 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

16 Part D Denominator File Called the Master Beneficiary Summary File (MBSF) Contents 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. Beneficiary Demographic Summary File information (BSF) known to Chronic Conditions CMS overwrites (26) all demographic information from a claim or Part D event Cost & Utilization in the MBSF. annual summary That means agreement between MBSF Death Information 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 Warfarin

20 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

21 Part D Appended Files Prescriber Encrypted ID, type, specialty, subspecialty, and state The encrypted ID for prescribers, plans, and pharmacies does not link with A or B files or with any Encrypted other beneficiary- level ID, benefits, premiums, files. tiers, service areas Plan Pharmacy Encrypted ID, type of pharmacy, state

22 PART D QUIZ True or False: Medicare beneficiaries with Medicaid (dual eligibles) continue to get their prescriptions through Medicaid.

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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 Medicare savings programs

25 Identifying Low Income Beneficiaries State Buy- In: A state paid for the beneficiary s Part B coverage through Medicaid or a savings program How to identify: State Buy- In: State buy- in variable in BSF LIS: Cost share group variable Dual enrollment Eligible: State reported dual eligible status code Low Income Subsidy: benes with help paying premiums, deductibles, and/or copay, no gap, no late Dual Eligible status: traditional Medicaid and other Medicare savings programs

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 D. 30

27 What drugs are covered? No OTCs Protected Drugs: Immunosuppressant Antidepressant Antipsychotic Anticonvulsant Antiretroviral Antineoplastic

28 What drugs are covered? No OTCs Protected Drugs: Benzodiazepines and barbiturates are not covered under Medicare Part D, but they may be covered by some states LIS programs Immunosuppressant Antidepressant Antipsychotic Anticonvulsant Antiretroviral Antineoplastic

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30 Tier Structure

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32 Medication- Centered Research Ideas with Part D Polypharmacy Overutilization Use of inappropriate medications Suboptimal prescribing Drug interactions Underutilization Adherence Prescribing patterns

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 abributable to the provider.

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 months Part D in 2007 PDE files in 2006 and 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 Excluded hospitalized and ins<tu<onalized persons

36 Measure of adherence: MPR Medication Possession Ratio: (MPR) # days supply dispensed between 1 st and last fill date # days between 1 st and last fill date Exclude days supply on last fill date 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 1 st and last fill / total days between 1 st and last fill Main outcome = % adherent Adherent = average MPR 80% or greater Possible predictors Demographics Socioeconomic status Comorbidity Medica<on use Number of prescribers

38 Medicare enrollees with PDE files in n=2,169,874 Medicare beneficiaries with HTN n= Age 66 on 1/1/07 n= , benes without hypertension 88,251 younger than 66 on 1/1/07 24 months of Part A and B n= Part D events for blood pressure medica<ons n= ,580 with HMO 14,900 no BP meds 27,810 with <2 claims 206 with dose change Uncomplicated HTN n= Uncomplicated HTN N= Study Popula<on N= ,125 comp. HTN 2160 with MPR > ,651 hospitalized ,550 in nursing home 324 in Territories or unknown

39 Table 1. Characteristics of Study Population (n=168,522) Variable AGE GROUP SEX RACE/ETHNICITY Category Total (Column %) Percent Adherent (21.6%) 78.9% (25%) 79.3% (22.5%) 79.7% (16.9%) 79.2% (13.9%) 80.5% Female (69.4%) 79.4% Male (30.6%) 79.6% Non- Hispanic white (81.9%) 81.5% Black (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 Variable LOW- INCOME SUBSIDY MEDIAN INCOME IN CENSUS TRACT % IN CENSUS TRACT WITH <12 YEARS EDUCATION Category Total (Column %) Percent Adherent East North Central (16.6%) 82% East South Central (8.1%) 77.3% Middle Atlantic (12.1%) 80.4% Mountain 7189 (4.3%) 78.2% New England (6%) 83% Pacific (10.7%) 76.7% South Atlantic (21.2%) 78% West North Central (10.2%) 84.2% West South Central (10.9%) 75.6% No (75.8%) 80.5% Yes (24.2%) 76.1% 0-31, (22.1%) 76.1% 31,000-38, (25.9%) 79.8% 38,000-49, (25.7%) 80.6% 49, (26.4%) 81% (25.3%) 81.7% (24.9%) 81.6% (24.9%) 79.5% (24.8%) 75.2%

41 Table 1. Characteristics of Study Population (n=168,522) Variable DEPRESSION DEMENTIA NUMBER OF ELIXHAUSER S CONDITIONS IN COVERAGE GAP IN 2007 NUMBER OF MEDS NUMBER OF BP MEDS Category Total (Column %) Percent Adherent No (92%) 79.6% Yes (8%) 78.2% No (95%) 79.5% Yes 8451 (5%) 79% 0-1 comorbidity (44.5%) 80.4% 2-3 comorbidity (36.9%) 79.3% 4+ comorbidity (18.7%) 77.6% No (64.1%) 77.2% Yes (35.9%) 83.5% 8.9 (+/- 4.8) 2.1 (+/- 1.1)

42 Logistic Regression Model for Adherence (n=168,522) Characteristic Odds Ratio (95% CI) Low Income Subsidy 1.14 ( ) % in Census Tract <12 yrs Education 0.93 ( ) (>18.6% vs %) Depression 0.94 ( ) Comorbidity (0-1 conditions as reference) 2 to 3 conditions 0.93 ( ) 4 or more conditions 0.85 ( ) Number of Medications 0.97 ( ) In the Coverage Gap in ( ) Total Copay ($1000 incr.) 1.23 ( ) Number of Unique Prescribers 0.98 ( )

43 Regional Differences in Adherence Percent of Population in HRR with 80% Adherence % ADHERENT percentile 20th 50th 80th

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% Model with HRR and Pa<ent Factors: ICC = 0.87%

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 of prescribers.

46 What is the gap?

47 Pedan et al. Am J Manag Care 2009;15:

48 Pedan et al. Am J Manag Care 2009;15:

49 Medication- Centered Research Ideas with Part D Polypharmacy Overutilization Use of inappropriate medications Suboptimal prescribing Drug interactions Underutilization Adherence Prescribing patterns

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 drugdisease combinations) Unable to assess over-the-counter meds

52 Data Elements Variables Design and Methods Data Source Patient Age, sex, race/ethnicity, state buy- in PDE denominator Comorbidities (Elixhauser s Index) Hospitalization in 2007 PIM in 2008 according to Beers list 2007 carrier file and MEDPAR MEDPAR 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 Results: Study Flow Chart Texas Medicare Part D Beneficiaries Age 66 in 2008 N = 2,261, 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, or more beneficiaries per physician prescriber N = (24,561 MD/DO)

54 Results: Number of Beneficiaries Per Prescriber Distribution of BID_PCT_PROVIDER & CLM_PCT_PROVIDER Percent of prescribers Percent BID_PCT_PROVIDER Number of beneficiaries per prescriber

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 % of the 24,561 prescribers prescribed at least 1 PIM

56 Table 1: Characteristics of 677,580 Beneficiaries PIM poten<ally inappropriate medica<on Characteristic Category Number % Getting a PIM Age , , , , , Sex Female 441, Male 235, Race/Ethnicity White 465, Black 52, Hispanic 139, Asian 16, Other 3, PIM use increased with increasing age, and differed between sexes and categories of race/ethnicity

57 Table 1 (cont d): Characteristics of 677,580 Beneficiaries PIM poten<ally inappropriate medica<on DM Diabetes mellitus PVD peripheral vascular disease Characteristic Number % Getting a PIM State Buy- in in 2008 YES 206, NO 471, Hospitalization in , Comorbidities Heart Failure 108, Total Number of Medication Claims (SD) Uncomplicated DM 213, Complicated DM 81, Hypertension 523, Pulmonary Disease 150, PVD 133, Depression 70, Cancer 72, Psychoses 43, Neurologic Disorder 91, (+/- 32.0) 52.2 (+/- 35.9)

58 Table 2: PIM Use According to Number of Prescribers Number of Unique Prescribers Number of Beneficiaries % of Beneficiaries Getting a PIM 1 182, , , , % of beneficiaries had >1 prescriber for all prescriptions PIM use increased considerably with increased numbers of unique prescribers

59 Table 3: Prescriber Characteristics and PIM Use Characteristic of Prescriber Number of Prescriptions % of Beneficiaries Getting PIMs Credentials MD 1,753, Specialty DO 133, Gen. Internal Medicine 355, Family Medicine 438, General Practice 20, Internal Medicine Specialty 364, Geriatrics 30, Gynecology 27, % of all beneficiaries who got a prescription from an MD got a PIM from that MD

60 Table 4: 10 Most Commonly Prescribed PIMs PIM Name Propoxyphene Nitrofurantoin Clonidine Cyclobenzaprine Amitriptyline Doxazosin Amiodarone Dicyclomine Carisoprodol Methocarbamol Number of Beneficiaries 83,415 37,908 28,496 27,893 19,390 11,941 10,

61 Table 5: Multivariable Model for Odds of PIM Use Characteristic Odds Ratio 95% CI Age Ref Gender Female Male 1.0 Ref State Buy- in Yes No 1.0 Ref Race/Ethnicity White 1.0 Ref Black Hispanic Asian Other

62 Table 5 (cont d): Multivariable Model for Odds of PIM Use Characteristic Odds Ratio 95% CI Hospitalization in Heart Failure Uncomplicated DM Complicated DM Hypertension Pulmonary Disease PVD Depression Cancer Psychoses Neurologic Disorder 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.

63 Table 5 (cont d): Multivariable Model for Odds of PIM Use Number of Unique Prescribers Odds Ratio 95% CI Ref Increasing number of unique prescribers remained a strong independent predictor of PIM use in the multivariable model for patient factors.

64 Results: Adjusted % of Beneficiaries on PIMs Across All Prescribers Adjusted % of beneficiaries geing a PIM 10 th percen'le = 13.4% 90 th percen'le = 30.0% 607 (5.7%) Prescribers (N=10747) 1113 (10.4%) 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 Compare PDP and MAPD enrollees

66 Using MAPD Data Risk adjustment is still possible with Rx- Risk Johnson M et al. Med Care 2006

67 What can you do with Part D Data? Toxicities Cost Policy Access Disparities Adjustment for medication use

68 Questions? Thank you