research with medicare claim data

Research With Medicare Claim Data

Xinhua Yu, MD PhD

Division of Epidemiology and BiostatisticsSchool of Public HealthUniversity of Memphis

June 22, 2012

Outlines

• Medicare program overview • Bill process and claim data• Data structure and important variables• Requesting CMS Medicare data• Data analysis• Research applications• Discussion

1. Medicare Overview

Medicare Program

• National health insurance for age >=65, or people with certain disabilities, or people with ESRD etc.

• 1965 - Title XVIII of the Social Security Act• 7/1/1966 - Medicare Program started• 2003, Medicare Prescription Drug,

Improvement, and Modernization Act (MMA)• 2006 prescription drug program (Part D)

started

Medicare Coverage (Entitlement)

• Part A , or Hospital Insurance (HI)• Part B, or Supplemental Medical Insurance

(SMI)• Part “C”, or Medicare Advantage Plans (HMO,

PPO)• Part D, or Prescription Drug Coverage

Medicare Part A Benefits• Hospital care• Skilled nursing facility (SNF) care• Home health care– skilled nursing and rehabilitation care–patient confined to home

• Hospice care (added in 1983)– For terminally ill patients with a life expectancy of

6 months or less

Part A Eligibility• Elderly

– Person is eligible if they or their spouse worked 40, or more, quarters in their lifetime and paid Medicare tax while working

– For those who did not work 40 quarters, enrollment is possible by paying a monthly premium

• Disabled – a person who has received Social Security disability benefits

for 24 months

• ESRD- persons with end-stage renal disease, ALS - persons with Amyotrophic Lateral Sclerosis (ALS), or Lou Gehrig’s Disease

Part B Benefits

• Physician services (including nurse practitioners, physician assistants etc), and services provided by other providers (e.g., health departments)

• Facility charges for hospital outpatient services and ambulatory care centers– Note: a person who is seen in a hospital or hospital

outpatient setting will generally generate two claims, one from the facility and one from the physician

• Durable Medical Equipment• Must pay a premium to be enrolled in part B

Medicare Funding and Payment• Part A: Medicare Hospital Insurance Trust Fund

(Medicare tax)– 98% people >=65 are enrolled in part A

• Part B and D: Supplementary Medicare Insurance Fund (beneficiary premium and congress appropriation) – 96% elderly part A beneficiaries are enrolled in part B– ~60% elderly enrolled in Part D

• Deductable and coinsurance

Types of Medicare Program• Fee-for-service (FFS) or traditional Medicare

program• Medicare managed care (now Medicare

Advantage plan, Part C) began in 1985– Risk based: insurance co. receive a capitated money,

and plan assumes financial risk – Cost based– 12-16% of beneficiaries are in managed care– Higher in west coast (CA, OR etc.)– Medicare claims are likely incomplete for these

managed care enrollees, thus often excluded in the analysis

2. Bill Processing and Claim Data

Bill and Claims

• Claims are bills for services given to the Medicare enrollees

• Claims are processed sequentially and through hierarchical system

• Help understand the contents of Medicare data and validity of data fields

Type of Services• Institutional– Hospital Inpatient – Hospital Outpatient– Skilled Nursing Care– Home Health Care– Hospice

• Non-Institutional– Physician, Laboratory and Other Supplier Services– Durable Medical Equipment

Response

Institutional ProviderNon-institution Provider

Medicare Administrative Contractor (MAC)•Fiscal Intermediary

•Carrier

CWF Host

CMS

• Enter claim into system• Perform consistency and utilization edits• Calculate payment• Deny claims based on Medicare policy• Return denied claims to provider

• Update entitlement data• Check claims for entitlement, deductible, remaining benefit, and duplicates• Authorize full payment, partial payment, denial, or request additional data

• Update EDB with entitlement data• Add claims to National Claims• History Repository (NCHR)

Treatment

Claim (daily)

Claim

Claims data(Weekly)

Payment/Denial

Entitlement data

(Daily)

MedicareBeneficiary

Claim Forms• Uniform bill: UB-92/UB-04, for institutional providers (e.g.,

hospitals, Skilled Nurse Facilities, home health, hospice)– Facility (institutional) claims – Used to be processed through Fiscal Intermediaries

• CMS-1500 form: for non-institutional providers (e.g., physicians, lab, ambulance services, medical equipment bills)– Non-institutional claims– Used to be processed through Carriers

• 23 Medicare Administrative Contractors (MACs) process both bills– 15 MACs for part A and B, 4 MACs for DME, and 4 for Home

health and hospice• Components are different between these two forms

Research Claim Files• SAF: Standard Analytical Files, i.e., claim based

files– Contain “final action” claims– Inpatient, outpatient, physician services etc.

• MedPAR: Medicare Provider Analysis and Review– Each observation contains aggregated data of all

facility claims related to one episode of care– An episode of care is either a hospital or skilled

nursing facility stay.

SAFs and MedPAR• Each SAF contains “final action” claims– All adjustment (partial pay, denial, amendment) are

rolled up into one record• SAF is available for each type of services• For inpatient services– SAF is more detailed (e.g., attending physician ID)– But MedPAR is easier to work with• 99% of inpatient SAF contain only one record for each

hospital stay, thus essentially the same as MedPAR• Requesting SAF costs more than requesting MedPAR

Example: Emergency Room Visit• ER services are considered outpatient services– But ER is usually attached to a hospital– Billed using facility forms (UB-92/04)– Outpatient SAF

• What if ER results in a hospital admission?– Becomes Part A (hospitalization services)– Inpatient SAF/ MedPAR

• Physician services are Part B – Carrier files will have them

• So you need all files to capture diagnosis, procedures, and discharge destination for an ER visit

3. Medicare Data Structure

http://www.ccwdata.org/data-dictionaries/index.htm

3.1 Beneficiary Summary File

Beneficiary Summary File (Denominator File)

• A calendar year file (cross-sectional file)• All eligible Medicare beneficiaries who ever

enrolled (>= 1day) in Medicare – Limited by the criteria you requested

• Served as the denominator for calculating rate or prevalence

• Contains basic demographic, coverage, HMO, and part D enrollment information (discussed later)

Demographic Variables• Encrypted beneficiary ID– Encrypted from HIC (11 digit unique identifier that is

related to SSN)– Can be linked with multiple claim files

• Date of birth (age)– There are disabled people with age <65

• Sex• Race• Sources: social security administration (SSA),

railroad board (RRB)

Age• It is better to calculate the age variable by

yourself based on date of birth– The age variable in the file is calculated as of Dec. 31

in the previous year, thus misclassify those turning 65 during the study year as 64

• Something wrong with really really old people– Medicare had higher percent of people with >100

than the census• There are people with age >120 which is still very unlikely• i.e., some deaths are missed• Could be excluded in the analysis (a very small population)

Race/ethnicity• Since 1994, race codes were: white, black, Asian,

Hispanic, Native American, other, unknown• The sensitivity for the Hispanics code is estimated about

35%, i.e., only one third of Hispanics recorded themselves as Hispanics• But specificity is very high, i.e., if they claim themselves Hispanics, they

are almost sure Hispanics

– Many people claim themselves as other• No penalty for doing that

– Research Triangle Institute Race variable• Higher sensitivity (60%+) for identifying Hispanic population

Sex

• Sex is coded 1=male 2=female• There are no missing values for this field• Persons with missing information have it filled

according to the rule: if age is less than 65 and sex missing then sex=male if age is greater than or equal to 65 and sex is missing then sex=female– Thus there are “female” people with prostate

disease

Mortality• Date of death• Date of death validation indicator (“V”)• If date of death is not empty, beneficiary is died– 100% deaths are validated– But about 96% of death dates are validated– Survival time may be over-estimated if unvalidated

date of death is recorded as end of month• Source is from SSA and claim info (e.g. hospital

discharge status is dead)

Medicare Enrollment Status • Medicare Status Code (MSC) combines current

entitlement and ESRD– 10= Aged w/out ESRD– 11= Aged w/ ESRD– 20= Disabled w/out ESRD– 21= Disabled w/ ESRD– 31= ESRD only

• Often we excluded those with ESRD as they have different health care utilization patterns

• Disabled with age <65 are often excluded as well– Many of them are in Medicaid as well

State Buy-in• Medicaid paying Medicare premiums• All states exercise the option of paying Medicare

premiums for at least some people• This can take 3 forms:– State pays premiums only (5%)– State pays premiums and cost sharing (45%)– State provides full Medicaid benefits (50%)

• Monthly indicator (buy-in part A, B, or both)• Those with state buy-in can e assumed to have

lower income

Benefit Coverage/Enrollment Indicator• Monthly entitlement/buy-in indicator – Not entitled (0)– Part A only (1)– Part B only (2)– Part A and Part B (3)– Part A, State buy-in (A)– Part B, State buy-in (B)– Parts A and B, State buy-in (C)

• Also summary month counts for part A,B and buyin • 94% have both Part A and Part B– Often we limited the study to this population– Part A is entitled, while part B is not required

Examples: bene_mdcr_entlmt_buyin_ind

• CCCCCCCCCCCC (12 months, A&B SBI)• 333333333333 (12 months A&B)• 111111333333 (5 mon. A, then 7 mon A&B)• 111111111111 (12 months A)• 333300000000 (4 mon A&B,8 mon not elig)• 000000000033 (10 mon not elig,2 mon A&B)• 333333330000 (8 mon A&B, 4 mon not elig)

HMO indicator

• Monthly HMO enrollment indicator• Those in the HMO often have incomplete

claim history– Claims are not required to be submitted to CMS or

not released from CMS• a summary count of Months HMO coverage• No information on the actual managed care

types and plans• 12-16% of HMO enrollment

Examples of Monthly HMO Indicators

• 000000000000 (never in MCO)• 111111111111 (12 months non-lock-in)• 00000CC00000 (months 6 & 7 in risk MCO)• CCCCCCCCCCCC (12 months in risk MCO)• 00000CCCCCCC (months 6-12 in risk MCO)

Dual Eligible Status

• Eligible for both Medicare and Medicaid– Medicaid is means based: i.e., primary for people with

income lower than some standard, or needs based• Some dual eligible are in HMO or managed care• Dual eligible variable is better to identify low

income patients than state buyin– Dual eligible variable identify more low income

patients

Beneficiary Residency • Available in Research Identifiable file (RIF)• State, county and ZIP code of residence are the

mailing address for official correspondence– From SSA data– Some persons have their mail sent to another person

(e.g., son, daughter, guardian)• Analyses comparing state of treatment with state

of residency generally show high concordance• Always use denominator residence information– Residence info on other claims is not validated

3.2 Institutional Claims

Type of Claims• Institutional (facility) claims: UB-92 /UB-04 forms– Inpatient– Outpatient– Skilled nursing facility– Home health agency– Hospice

• Non-institutional claims: CMS 1500 form– Physician (and other providers) services– Lab tests and diagnostic exams– Durable medical equipment (DME)– Standard alone ambulatory services

UB 92 /UB-04 Form

• Patient demographics• Provider (hospital) ID and location (zip)• Admission/discharge date• Disease diagnosis and procedure: ICD-9 codes• Detailed services (revenue centers in SAF)• Payment and coinsurance• Discharge destination

Hospitalization/MedPAR• Medicare Provider Analysis and Review – Short-stay/Long stay hospitals

• Short stay 85%• Long stay hospital 2%

– Skilled Nursing Facility (SNF) 13%• Reimbursement for SNF is different (per diem based)

• One record per hospital stay in MedPAR– One stay may consist of several records in Inpatient

SAF, but these are small proportion• Categorized payment info in MedPAR– Original revenue center codes in SAF

Finding Provider (Hospitals)• Organization NPI– Intelligence free identifier – HIPPA compliant

• PRVDR_NUM variable – 6 columns: SSA state (2)+type of facility(4)

• Traditional acute care hospitals: 0001-0879 • critical access hospitals: 1300-1399• Critical access hospitals may not use PPS

• Short stay hosp, long stay hosp, and skilled nursing facility (SS_LS_SNF_IND_CD)– Need to separate them in analysis

Length of Stay/Admission and Discharge Dates

• LOS=discharge date – admission date– Plus one if the same day hospitalization

• LOS for SNF is different– SNF is paid as per diem based on resource

utilization groups (RUGs) and has limit in days of stay

Diagnosis, Procedures and DRGs

• Clinical information available in four sources:– Medicare Severity Diagnosis Related Group (MS-

DRG) (1 per stay, per record)– ICD-9 diagnoses (up to 10 codes: 1 primary, 8

secondary, 1 injury code)– ICD-9 coded Procedures (up to 6 per claim)– Admission diagnosis code

• Diagnoses and procedures are consistent with DRG. However, not all DRGs require specific diagnoses

Example: AMI

• Almost all persons with primary discharge diagnosis of 410 have following DRGs:– 231-236: CABG with PTCA– 237-238: Major Cardiovascular Procedure

• Diagnosis, procedure and DRGs can be used to define distinct population

ICD-9 V codes• “Supplementary Classification of Factors Influencing

Health Status and Contact with Health Services”– 23% of hospitalizations have some V code– 2.8% have a V code as their primary reason for hospitalization

• Examples:– V56.0 Renal dialysis– V58.1 Chemotherapy– V58.61 Long-term use of anticoagulants– V59.4 Kidney donor– V67.4 Follow-up examination after treatment of a fracture– V70.2 General psychiatric examination

Hospital Charges& Payments• MedPAR contains 34 fields describing charges– Total charges– Total accommodation charges– Total departmental charges– Specific charges for accommodation sub-types and specific

departments or groups of departments• Patient’s payments– Inpatient deductible – coinsurance amount

• CMS– total reimbursements – bill total per diem

• Primary Payer (other than CMS) amount

Estimating Payments from MedPAR• Total paid by CMS:– total reimbursements + bill total per diem

• Total paid by the beneficiary:– inpatient deductible + coinsurance amount+blood

deductible• Total paid by all sources:– total reimbursement+ bill total per diem + inpatient

deductible + coinsurance amount +blood deductible + primary payer amount

• Note: Physician charges/payments are not in the MedPAR

Charge/Reimbursement Ratio• Most hospitals are on Prospective Payment

systems (PPS)– per stay payment based on DRG (include labor and

non-labor cost, with some geographic and risk adjustment)

– Claim PPS_IND_CD • Charge and reimbursement ratio for specific

hospitals may not be meaningful– But population wise, we often use this (or derived)

ratio to obtain estimated payment in hospital discharge data (e.g., cost/discharge ratio)

Categorized Cost Variables In MedPAR

• Cost unit: e.g.,– Intensive care unit indicator– Coronary care unit indicator– Diagnostic Radiology– CT/MRI– DME use

• Indicators for certain service use:– Pharmacy– Physical therapy– Laboratory– Emergency room

Discharge Destination• Information provided by hospital– Home/self care– Other short-term general hospital– Skilled nursing facility (SNF)– Intermediate care facility– Other institution– Home health service care– Left AMA– Home IV drug therapy– Died

Additional Comments on MedPAR• People admitted to hospitals through ER or outpatient

visit (planned or unplanned) will appear in MedPAR/inpatient SAF, often not in the outpatient claims– Check admission type variable

• Info in MedPAR is care received, not care needed• Some disease diagnoses may be missing, or some

conditions may not be diagnosed or recorded (e.g., hypertension)

• Combining with other claims, MedPAR is often a start point (e.g., studying the follow up care for those with CABG surgery)

Outpatient Claim File• Facility claims, use UB-92 /04 forms• Data structure is the same as inpatient SAF• CMS provides data in two files:– Base claim– Revenue centers (detailed info and charge)– Can be linked by bene_id and claim_id

• If you request CCW data– Chronic condition files: condition, span and health

care cost/values

Basic Claim File• Patient demographics• From and through date• Provider number• Attending and operating physician NPI• ICD 9 diagnosis and procedures (up to 25)• Total charge and payment • Discharge status

Physician NPI• National Provider Identifier (NPI)– Unique ID (intelligence free identifier)– Note: in old data, physician UPIN etc.– Not encrypted in Research Identifiable Files(RIF)– Can be linked with AMA master file and other

commercial physician data• Usually attending physician NPI is used – Operating physician NPI may be useful in surgery if

they are different

Revenue Center files• Multiple records per claim– One basic claim record is linked to many revenue

records (up to 450 revenue codes)– Matched with claim file by bene_id and claim_id • Indicated by the number of line variable as well

• Variables include revenue center, procedure performed, modifiers, service units, charge and payment per unit

Revenue Centers• Are institutional cost centers for which separate

charges are billed– Facilities are not required to have every revenue center

reported because overall payment is based on DRG• Examples: – 0141 Private room, medical/surgical– 0258 Pharmacy, IV solution– 0305 Laboratory, hematology– 0350 CT scan, general classification– 0382 Whole blood– 0961 Professional fees, psychiatric

HCPCS: Health Care Common Procedure Coding System

• Also called HCFA common procedure coding system• Include:– current procedure terminology (CPT ) – and some additional level II and III codes created by CMS

• More detailed than ICD-9 procedures• Change over time: some are added, some are

abandoned• Used in billing for revenue center, physician services,

etc.– In outpatient claim files, ICD9 procedure codes are not

complete and validated. Better use HCPCS in revenue center files for searching procedures and outcomes

HCPCS examples• Level 1: CPT codes:– 00100 -01999 Anesthesia– 10040 - 69990 Surgery– 70010 - 79999 Radiology– 80049 - 89399 Pathology and Laboratory– 90281 - 99199 Medicine– 99201 - 99499 Evaluation and Management

Example: HCPCS• Level 2 Codes – A0000 - A0999 Transportation Services including

Ambulance– A4000 - A8999 Medical and Surgical Supplies– A9000 - A9999 Administrative, Miscellaneous and

Investigational – B4000 - B9999 Enteral and parenteral therapy– Preventive services• Influenza vaccine 90724• Influenza vaccine administration G0008

– Chemotherapy: J codes• Level 3 codes

HCPCS Modifiers• Can have up to 4 modifiers• Related to charge and payment• Level 1 – numeric: e.g., – 21 - Prolonged Evaluation and Management

Services– 26 - Professional Component

• Level 2 - alpha or alpha-numeric: e.g., – TC - Technical Component– LT = left, RT = right

Services Unit and Payment• For each revenue center and associated

procedure– Number of services units, payment per unit– Charge and payment for each revenue center– Patient deductible, coinsurance, and provider

responsibility, in addition to CMS payment• Good for detailed analysis at revenue center

level

3.3 Non-Institutional File

Carrier Files• Physician services, lab test, exam, and supplier• Part B services• Billed using CMS 1500 form• Include two linkable files– Basic claim file– Line item file for detailed services and payment

• Stand-alone ambulatory surgical center also in this file

Carrier Basic Claim File• Bene_id and claim_id• Patient demographics• From and through date• Claim ICD-9 diagnosis codes (up to 12)– Principle diagnosis indicated

• Claim total charge, CMS allowed charge, and CMS payment– Patient portion of payment (deductible) as well

Carrier Line Item File• Linked with Carrier_claim file by bene_id and

claim_id• Multiple records is matched with one claim– Up to 13 line items– Each line item is one record in line file

• Usually the largest file of all claim data• Date of services• ICD-9 diagnosis and HCPCS procedure for each

line of services• Physician information• Charge and payment

Line Diagnosis and Procedure

• Can up to 13 line items for each claim• Line diagnosis should be included in the claim

diagnosis as well• HCPCS codes and modifiers are used in

physician services– Most useful for searching procedures performed– Lab test, diagnostic exams based on HCPCS

BETOS codes

• Purposefully aggregated based on HCPCS codes

• Berenson-Eggers Type of Service (BETOS) codes

• Useful in tracking different types of services– Charge & payment– Utilization patterns– Relatively stable

Examples of BETOS codes• M1A = Office visits - new• M1B = Office visits - established• M2A = Hospital visit - initial• M2B = Hospital visit - subsequent• M2C = Hospital visit - critical care• M3 = Emergency room visit• M4A = Home visit• M4B = Nursing home visit• M5A = Specialist - pathology• M5B = Specialist - psychiatry• M5C = Specialist - opthamology• M5D = Specialist - other• M6 = Consultations• P0 = Anesthesia

Physician Information

• Referring physician and performing physician NPI– Performing physician NPI is complete– Although referring physician ID is required, but

self referral is OK– NPI is not encrypted

• Group NPI for group practice – Its usefulness is complicated

Example: Line Physician Specialty 01 = General practice 02 = General surgery 03 = Allergy/immunology 04 = Otolaryngology 05 = Anesthesiology 06 = Cardiology 07 = Dermatology 08 = Family practice 10 = Gastroenterology 11 = Internal medicine

Example: Line Place of Service 11 = Office 12 = Home 21 = Inpatient hospital 22 = Outpatient hospital 23 = Emergency room - hospital 24 = Ambulatory surgical center 31 = Skilled nursing facility

Line Type of Services

• Distinguish different type of services– 1 = medical care– 2 = surgery – 3 = Consultation – 4 = Diagnostic radiology – 5 = Diagnostic laboratory – 6 = Therapeutic radiology – 7 = Anesthesia– Etc.

Line Charge and Payment• CMS payment, beneficiary payment, provider

payment, and primary payer codes• Line Allowed Charge Amount - the charges

allowed by CMS• Line NCH Payment Amount - the amount paid by

CMS– CMS actual payment is generally 80% of allowed

charge for physician services. Patients have copay/part B deductible, coinsurance etc.

– For Lab test, they are the same

Line Service Units• Carrier Line Miles/Time/Units/Services

(MTUS) count– Actual counts of service units– Ambulances are based on miles

• Carrier Line Miles/Time/Units/Services indicator code – e.g., 0=not allowed unit, 1=transportation, 2=

anesthesia time, 3 = number of services, 4= oxygen volumes, 5=blood units

– Majority are 3

3.4 Part D data

Medicare Part D data

• Medicare drug benefit• Data is processed and managed differently

from traditional Medicare Part A/B files• Files:– Prescription drug event (PDE) file (can be linked

with Medicare claims)– Drug characteristics– Plan characteristics

Part D Enrollment

• Monthly patient enrollment information (in beneficiary summary file)– Contract ID, Plan benefit package ID, segment ID

and cost share group– Can be linked to plan, drug, prescriber and

pharmacy characteristics

Medicare Part D enrollment, 2010

Monthly Drug Plan Contract ID

• Unique to each plan • Indicates what types of plan, based on the first

letter of the contract ID:– H: local MA-PD, PACE, cost plans and demo– R: Regional MA-PD– S: PDP (prescription drug plan)– N: Not Part D enrolled (no part D data)– E: Employee-sponsored plans– 0: not enrolled in Medicare (no part D data)

Cost Share Group• Indicate whether subsidy is provided

– 00 = Not Medicare enrolled for the month– XX = Enrolled in Medicare A and/or B, but no MIIR record for the

month– 01 = Bene is deemed with 100% premium-subsidy and no copayment– 02 = Bene is deemed with 100% premium-subsidy and low copayment– 03 = Bene is deemed with 100% premium-subsidy and high

copayment– 04 = Bene with LIS, 100% premium-subsidy and high copayment– 05 = Bene with LIS, 100% premium-subsidy and 15% copayment– 06 = Bene with LIS, 75% premium-subsidy and 15% copayment– 07 = Bene with LIS, 50% premium-subsidy and 15% copayment– 08 = Bene with LIS, 25% premium-subsidy and 15% copayment– 09 = No premium subsidy nor cost sharing = not LIS– 10 -13 = not in Part D

Part D Event Data• Contain drug filled (not actually taken)• Detailed medication data based on pharmacy bills, but

not exactly the same as the pharmacy claim and so differs from point-of-service – Post-transaction adjustments between plan and pharmacy– Plan-to-plan adjustments for misenrollees– Plan-to-CMS adjustments for some demonstration projects

• 37 variables: prescription date, national drug codes (NDC), dosage, brand names, generic names, days supply, payment, and coverage indication (gaps?)

Drug Codes• PROD_SRVC_ID: National drug codes (11 bytes)

Other Drug Information

• Both brand names and generic names– Need to use fuzzy search for drug names

• First Data Bank therapeutic class– Useful in grouping drugs: e.g., anti-diabetic drugs

• Drug dosage• Drug strength

Medication Days and Supply

• Prescription Service Date (SRVC_DT)– Prescription initiation date

• Prescription Days Supply (DAYS_SUPLY_NUM)– Key variable to construct medication adherence

measures– Median/Mode: 30 days

• Prescription amount (Quantity Dispensed)– Not very useful so far

Medication Adherence

• Several measures have been propose• Medication Possession Ratio (MPR) Proportion of

Days Covered (PDC)– Proportion of days supply during a specified time

period or over a period of refill intervals• One year? Months? Study period?• Overlapping days?

• Medication Gaps:– The proportion of days without medication during a

specified time period or interval

Medication Utilization Management Information

• Quantity limit: plans limit the numbers (or amounts) of a drug in a given time period

• Prior authorization: preapproval is required before coverage

• Step therapy (maximum step number): specified drugs should be tried before moving to other drugs

• About one third prescriptions subject to utilization management

Payment and Coverage

• Gross drug cost (total cost) :– includes patient payment, other true out-pocket

payment, low income cost-share (subsidy), patient liability reduction due to other payer amount, covered part D payment, and uncovered payment

Characteristics Data

• Drug characteristics– Linked by drug codes, including strength, dosage,

brand names, generic names etc. (appended to PDE file)

• Plan characteristics– Includes plan type, benefit design, premium, cost-

sharing and service area of Part D plans• Prescriber (provider) and pharmacy files– Basic characteristics of providers and pharmacy

Note on Requesting Drug Data

• Need to plan carefully on what variables and what files you want to request– CMS requires justification for every variable

included request– CMS charges differently based on how many

variables you request

3.5 Comments

Other Claim Data• Home health agency (HHA) and Hospice data

are facility claim data, similar to inpatient and outpatient SAF– But the payment system are different

• DME files are the same structure as Carrier files

• Part D (drug) data follow different structure– Can be linked with Medicare claims by bene_id

Advantages of Medicare Claim Data• Claim data include services covered and received,

payment, and disease information, in addition to patient demographics

• Almost complete elderly population in the US– Only limited by your inclusion criteria and study

design• Can be combined with other data such as census

and large surveys or cohort study• Data available timely (usually available after June

next year)

Limitations of Medicare• Disease diagnosis and procedure but no other

clinically important information– Cancer staging, histology– Lab test or diagnostic exam results– No disease severity– Duration of disease is unclear (e.g., for chronic disease

such as diabetes, hypertension, unknown starting point)

• ICD-9 procedure codes are used in facility claim, while CPT/HCPCS codes are used in non-facility claims, complicating matching

Limitations (cont.)• Only covered services are included– Uncovered services are not reported

• No information for Part B services for managed care enrollees– Hospitalization for managed care enrollees are limited

and unknown quality• Charge and payment information are accurate

but diagnosis may be incomplete if they have no impact on payment

• Fraud exists– Outliers are not necessary fraud

4. Requesting CMS Data

Before You Request…• Having a research proposal• Know the data and how to analyze them• Having sufficient funding– CMS Medicare data cost a lot (~$14,000 for one

year of total claims)• Allow some waiting time– Requesting process, CMS Privacy Board Review,

and data purchase may take 3 or more months

Researcher’s Tasks • Research proposal– Research goals– Data analysis plan

• Identify possible data source– Is Medicare data appropriate for this project?– Are there any other data sources that are better fit for

the project?• Research identifiable files– Most commonly used– Contain more information

Role of ResDAC• Email ResDAC about potential Medicare projects– Identify appropriate data source– Estimate sample size– Estimate cost

• Prepare requesting package– http://dev.resdac.umn.edu/Medicare/requesting_dat

a_NewUse.asp• Prepare for several rounds of modification• Consultation for free – Also helpful when writing research grant that uses

CMS data

Requesting Package• Written Data Request Letter• Study plan /protocol/executive summary• Data Use Agreement (DUA)• IRB approval / HIPAA waiver• Evidence of Funding• Specification Worksheet• CMS Cost Estimate• CMS Disclaimer User Agreement• ResDAC Review Letter

Data Use Agreement (DUA)

• Legal contract for use of CMS data– use data only for the purpose cited in the request– not to release CMS data to other organizations– details safeguards to prevent unauthorized access– obtain CMS review of findings prior to publication– return or destroy data by retention date

• Need to renew/extension with CMS every year

CMS Review Criteria• Potential for benefit to Medicare beneficiaries or the

Medicare program• Potential benefit outweighs risk to beneficiary privacy• Compliance with the terms of the DUA• Request data covered for release under the Privacy Act• Does not result in product that will be marketed• Manuscripts, presentations, any release of findings will be

submitted to CMS first– Highlight specific sections/tables

• HIPAA waiver criteria will be met

Other ResDAC Services• All sorts of CMS associated data• CMS data file content• Data extraction methodology• Data request process• Reading in the data• Data element interpretation • Data source, cost estimate during grant

writing

CCW data

• Chronic Conditions Warehouse (CCW)• 21 chronic conditions• 100% and 5% enhanced sample–Chronic Conditions Flagged–Control Group available–1999 to present available

• http://www.ccwdata.org/index.htm

Chronic Conditions• Acute Myocardial Infarction• Alzheimer's Disease • Alzheimer's Disease, Related Disorders, or Senile

Dementia• Atrial Fibrillation• Cataract• Chronic Kidney Disease• Chronic Obstructive Pulmonary Disease• Congestive Heart Failure• Diabetes• Glaucoma• Hip/Pelvic Fracture

Chronic Conditions• Ischemic Heart Disease• Major Depression• Osteoporosis• Stroke / Transient Ischemic Attack• Breast Cancer• Colorectal Cancer• Prostate Cancer• Lung Cancer• Endometrial Cancer• Osteoarthritis• Rheumatoid Arthritis

4. Data Analysis

Create Study Cohort• Study cohort can be defined:– Geographically: state, region– By time: calendar year– Demographically: age, race, sex– Clinically: • having certain diagnosis (e.g., diabetic patients)• Having certain procedures (e.g., CABG patients)

–Combination of the above

Example: West TN Beneficiaries

• Study cohort:– All elderly people residing in west TN in 2009 • List of county in West TN was used• Beneficiary county only

– Age: >=65 in 2009– People in HMO: excluded– Must have both Part A and Part B enrollment

Example: BPH Cohort• Patients who had benign prostate hyperplasia

(BPH) surgical procedures from 2002-2008– ICD-9 codes: TURP (60.29), TUMT (60.96) etc.– CPT codes: TURP (52601), TUMT (53850) etc.– Note: BPH diagnosis code (600) was not used as a

required diagnosis, as some BPH procedures did not have a 600 diagnosis, but other related diagnosis

• Need to search inpatient, outpatient, and Carrier (physician services) files for the above procedure codes– TUMT are often performed in office setting, not in

hospitals

Research Finder File• Finder file is used to request CMS data– Finder file defines your study cohort– Better be broad at the beginning (i.e., requesting

more than needed)• E.g., may include HMO, ESRD, etc.

• For geographically defined data, submit a list of state and county is sufficient

• For clinically defined data, submit the ICD-9 and CPT/HCPCS codes, and define what claim files and how you will search for these codes – Some clear algorithm is needed (consult ResDAC)

• Pre-defined cohorts with patient’s SSN – IRB issues

Linking with Different Files• All claims data (including drug data) can be

linked with Bene_ID• Basic claim files and revenue center or Carrier

line item files can be linked by bene_ID and claim_ID– Some roll up may be needed in revenue center or

line item files– Often we find specific services in revenue center

or line item files and then link these with basic claims

Matching Between Different Claims• To link physician services with surgical procedures

performed in hospitals (thus creating a complete episode), we need to match the date of services between different claim files, in addition to bene_ID and clinical services

• However, date of services in the claim files may be off slightly– A fuzzy matching by allowing +/- 3 days (or more) will

do

Linking with Other Files

• Area based files, using state, county, and zip codes– E.g., census, area resource file

• Individual based files, e.g., existing cohort study– Need SSN or HIC– Name, DOB may be possible too

Defining Outcomes• Easier to define Yes/No type of outcomes– AMI hospitalization (yes/no)?– Urinary stricture after BPH surgery? (yes/no)

• Usually need to search multiple claims files– MedPAR for hospitalization, outpatient and Carrier

files for physician services or complications• Matching the final outcome files to de-duplicate – Match by diagnosis, procedure and date of services

• Mortality data is usually valid– Some date of death may be not validated

Time to Event Outcome• Starting time is often the date of surgery– Admission date can also used if no date of surgery

• For outcomes resulting hospitalization or ER visit, the event time is the admission date on the claim

• For outcomes resulting only physician office visits or small procedures (e.g., complications), searching both outpatient and Carrier claims – Matching and de-duplicated process is needed– Event time is the earliest from-date on the matched claims

• Censored at the date of death or the last day of claim files (e.g., 12/31/2009)

Comorbidities, pre-existing Conditions, and Complications

• Can be difficult to distinguish between comorbidities and complications if they appear in the same claim– Need prior clinical knowledge to define

complications– Existing algorithms exist for getting comorbidities

• Combining with other claims (outpatient, physician services) to obtain complications

Identifying Comorbidities

• Charlson Score, the most common comorbidity index, can be applied to claims data

• Sum of number of comorbidities with some weights– Total 22 comorbidities

• Is calibrated to predict 1 year mortality• Most often, we categorize them into 0,1,2,3 or

more, due to small sample size in the 3+ group

Charlson Score Distribution

05

101520253035404550

%

0 1 2 3 4 5 6 7 8 9Charlson Score

General Algorithm for Obtaining Charlson Score

• Any related diagnosis in inpatient and outpatient services claims (facility claims) before the hospitalization (i.e., excluding the current one)

• Diagnosis appeared in at least two separate physician services (Carrier) claims (different dates)– Often excluding lab test, diagnostic exams to avoid

“rule out” diagnosis• Combining the indicators of Charlson Score

related diagnosis and Sum them over with weights

Other Methods for Calculating Comorbidities

• Direct search important comorbidities– E.g., AMI, stroke, diabetes, etc.

• Elixhauser comorbidities method (AHRQ)– Predicting inpatient LOS, hospitalization charge, death

based on CA data– 30 comorbidities, directly used as indicators in the

model• Generally, same algorithm as that of the previous

slide, with some modification in conditions

Forming Analytic Files• Combining patient socio-demographics,

original disease status and procedures, date of services, comorbidities, time to event and outcomes, and charges and payment into one record

• Inclusion and exclusion criteria• Warning: don’t forget those having no events– They are from denominator files– Often we search multiple claims for outcomes

5. Applications

Comparative Effectiveness Analysis• Compare outcomes among different treatments• Example: comparing rates of repeated

treatment and complications among elderly patients undergone BPH surgery– For minimally invasive BPH surgery, there is a higher

rates of re-treatment for BPH in long term (e.g., >5% for TUMT in five years)

BPH Cohort• Age >=66 at the time of surgery• BPH surgeries: TURP, TUMT, TUNA, Laser• Not died within 30 days of surgery• Having both Part A and B enrollment• Not enrolled in any HMO during the follow up– Seems limited too much

• Residing in 50 States (thus excluding those US territories)

Defining First BPH Surgery• Our main outcome is repeated BPH surgery• But our cohort starts at 2001, some patients may

already have a BPH surgery before then– Thus the “first” BPH surgery in our data may in fact

the repeated surgery– Reduce the rate of repeated surgery

• We request additional year (2000)– Search possible BPH surgery, remedial treatment, or

complications– If exists, then these patients may have BPH surgery

before– They will be excluded in the analysis

Defining Outcomes• Second BPH surgery • A list of BPH complications• Two types of outcomes– Yes/no outcomes, separated by period (e.g., 1 year, 3

years, and 5 years)– Time to event outcomes– Only first of same outcomes included (e.g., claims with the

same complication may be found at different time)• Censoring – Censored at date of death or last date of claim file

(12/31/2008)• The last year (2008) claims were used as follow up to

ensure everybody has at least one year of follow up. Thus BPH surgery occurred in this year were not included the study cohort

Comorbidities• Searching ICD-9 diagnosis in all claim files

(MedPAR, outpatient, Carrier)• One year before the date of BPH surgery– Thus we did not use one year of data for study

cohort– Age now starts with 66 at the time of surgery

• Charlson Score was calculated, and classified into 0, 1, 2, 3+

Socio-economic data

• Patient race, age, sex, and state buyin status• Census data were linked with denominator

files based on beneficiary residential zip codes• Zip code level income, percent of high school

education, and percent of blacks were used

Questions?