Medicare and Medicaid Enrollment and Claims files

1. Medicare and Medicaid Enrollment and Claims files

2. Note • Rather than a detailed description, these slides are intended to provide a broad overview of Medicare and Medicaid databases. • For detailed description of the contents of the file, please refer to the following website: • Resdac.umn.edu

3. Medicare program description • Program started in 1965 • Eligible population: • Elderly • Disabled • End-stage renal disease (ESRD) • The dually (Medicare-Medicaid) eligible • Growing program and increasing number of enrollees, with the aging of the population (> 40 million people) • More women than men

4. Program description, cont’d • Part A – inpatient hospital • Entitlement program • 98% of individuals 65 years of age or older are enrolled in Part A • Deductible applies for each spell of illness • Coinsurance applies • Part B – outpatient care • must pay to be enrolled in Part B • Payment deducted from soc sec check • 96% of elderly and 90% of disabled are enrolled in Part B • Deductibles and coinsurance apply (with the exception of some services)

5. Program description, cont’d • Part D – Drug benefit • Introduced in January 2006 to help address the needs of seniors with high out-of-pocket costs for medications • Potential coverage “gap” or so-called “doughnut hole” • Partial coverage for the first $2,250 of total drug costs in 2006 followed by a period of no coverage until patients reach cumulative out-of-pocket costs of $3,600 in 2006. • At the end of the “gap” period, “catastrophic coverage” begins (co-pay of 5% of drug costs or a pre-determined co-pay from that time onward) • ~18.2 million beneficiaries were enrolled in Medicare Part D in 2006. • * Schmittdiel et al. Am J Managed care, 2009; 15(3): 189-193.

6. Note on Dually Eligible population • Elderly people meeting certain eligibility criteria can enroll in state Medicaid programs, in which case Medicaid provides coverage for their premium, deductible and coinsurance (also referred to as “state-buy-in”) • Dually eligible people representing the poorest sickest and the frailest of the elderly population • Enrollment of the eligible elderly in Medicaid remains quite low • Implications in research…

7. Program description, cont’d • Supplemental medical insurance (SMI) purchased individually or in group, intended to provide coverage • for services not covered under Medicare • Medicare deductible and coinsurance • Services NOT covered by Medicare • Eyecare • dentures

8. Program description, cont’d • Managed Care • Capitation based, based on the Adjusted Average Per Capita Cost (AAPCC – 95% of fee-for-service (FFS) payment for the county, adjusted for age, gender, institutional status, and dual eligibility status) • Increased enrollment over time (~15%) • Managed care enrollment NOT uniform across geographic regions of the country • Absence of encounter level data  Enrollees represented in the denominator files, but not in claims files • IMPLICATIONS IN ANALYSIS

9. Medicaid program description Source: the Kaiser Family Foundation (www.kff.org)

10. DATABASE STRUCTUREEnrollment files • Beneficiary identifier • Demographics (age, race, sex) • Enrollment spans • Eligibility categories (Medicaid) • Participation in managed care / state buy-in (Medicare) • Dual enrollment, spenddown (Medicaid) • County / address of residence • Vital Status (Medicare) • One record for each individual enrolled in the program during a given calendar/fiscal year

11. Enrollment file, cont’d • In Medicare, the information is finalized at the end of the quarter following a calendar year – • NOT UPDATED BEYOND THAT POINT

12. Validity of demographic information • Denominator file is the recommended source of demographic variables for Medicare data analysis • Considered reliable for the most part • Age • Sex • Race (depending which category) • Place of residence (consider special category of elderly living half-time in Florida) • Vital Status -- date (not cause) of death

13. Note on HMO monthly indicators • Not possible to tell: • whether disenrollment is by choice, or whether managed care program discontinued service in area of residence • Whether disenrollment is due to death (indicators set to zero after death  check the date of death) • Switching between managed care plans

14. General use of the denominator file -- examples • Analyze shifts in demographics over time • Obtain denominators to calculate rates – by region, demographics, eligibility category • Obtain months on FFS vs MC => monitor managed care penetration rates over time and across regions (FFS months calculated as the difference between total months and MC months) • Obtain months on state buy-in (Medicaid eligibility – also referred to as dual eligibles)

15. DATABASE STRUCTURE(non-pharmacy) Claims Files • Beneficiary ID • Dates of service • Diagnosis codes • Procedure codes • DRG code • Revenue center codes • Length of stay • Charges • Reimbursement amount

16. Pharmacy claims files • Beneficiary ID • DOB, Gender • Paid date • Date of service • Service Provider • Compound code • Quantity dispensed

17. DATABASE STRUCTURE – CLAIMS FILES (organized by state fiscal year, based on date of service) I. Institutional Claims Inpatient Hospital, Outpatient Hospital, and ‘Other’ Institutional Header Detail line item II. Non-Institutional Claims Physician, Clinic, Lab, X-Ray, etc. Header Detail line item Drug Claims III.

18. MedPAR file • Hospital admissions for Medicare beneficiaries • One record per admission • Record carries: • Beneficiary ID • Demographics (age, sex, race) • Date of birth; date of death (if available) • Up to 10 ICD-9 diagnosis codes • Up to 10 ICD-9 procedure codes • Admission date; discharge date • Source of admission • Discharge status • Provider ID  possibility to link to American Hospital Assoc data to retrieve hospital characteristics.

19. MedPAR file, cont’d • Admission and discharge dates – useful; consistent; diff+1 agrees with LOS on record • Clinical information: • DRGs (1 per stay) • Diagnoses (ICD-9; primary discharge dx; 8 secondary dx; 1 injury) • Procedures up to 10 with corresponding dates • Admission diagnosis code • E codes to identify mechanism of injury coded inconsistently (depends of fiscal intermediaries) • Same applies to V codes (supplementary classification of factors influencing health status and contact with health services)

20. MedPAR file, cont’d • Pre-existing conditions and comorbidities can be identified using diagnosis codes • Difficulty to distinguish between pre-existing conditions and complications • Rule-out diagnosis (?) • Admission type – common codes • emergent (patient admitted through the ER); • urgent (patient admitted to the first available and suitable accommodation); • Elective (patient’s condition permitted adequate time to schedule availability of suitable accommodations)

21. MedPAR file, cont’d • Discharge status (alive; dead) • Discharge destination (e.g., home/self care; SNF; home health service care; left AMA; died) • Readmission vs. transfer • Detailed charge and reimbursement data

22. Part B files • Outpatient standard analytic file • Physician supplier / Carrier file • Important logistic issue: • (ENORMOUS) SIZE OF FILES, EVEN WHEN LIMITED TO SPECIFIC PATIENT COHORTS • Files available in • 5% sample (nationwide) • 100% sample (nationwide), only for specific patient cohorts • Pre-defined cohorts • Records carrying specific diagnostic/procedure codes

23. Part D files • Medicare ID • DOB, Gender • Paid date • Service Provider • Compound code • Quantity dispensed • Days supply • Catastrophic coverage code • Out-of-pocket payment

24. Claims data analysis

25. Consider origin of claims data • Derived from reimbursement or PAYMENT OF BILLS • Data elements needed to pay the bill will be of higher quality • COMPLETENESS • ACCURACY (?)

26. What is recorded in claims data? • Conditions that are DIAGNOSED • Care RECEIVED (care needed but not received will not be recorded in claims data) • Services that are covered • Prescriptions filled, rather than written • Services that are billed for by the provider •  Flu shots provided through grocery stores NOT recorded in claims data

27. What is recorded in claims data?, cont’d • Clinical information  limited • No data on physiology (e.g., vital signs) • Test results NOT included • Exact timing (of events) not included

28. Data Quality • DOES A DATA ELEMENT IMPACT PAYMENT? • YES  • Better quality (?) • Consistently recorded • Over-coding (?) • NO  • Quality (?) • Consistently recorded (?) • Also consider • Source of the data (Provider, fiscal intermediary, CMS) • whether a field is required, and/or validated or edited by fiscal intermediaries

29. Working from population-based files • Define population – who is your target population? • All persons in the numerator (events) must be eligible to be in the denominator • All persons in the denominator must be eligible to have the event • Consider newly eligible people; those who have died; those who move in/out of an area • Diagnostic criteria • Demographics • Specific coverage (Parts A, B, managed care) • Benefits program (elderly; disabled; ESRD) • Building cohort (Caution: consistency of IDs across time/files)

30. Working from population-based files, cont’d • Define outcomes of interest (mortality, readmissions, visits, procedures…) • Duration of time until outcome of interest (Survival) • Covariates • Demographics • Residence • comorbidities

31. Accuracy of claims data • Many studies to assess the accuracy of Medicare claims data • Comparisons between Medicare claims and • Medical records • SEER • State cancer registry • Sensitivity, positive predictive value differing by diagnosis/procedures • Higher sensitivity when including Part B data

32. Applications • Racial disparities (surgery for CR cancer; cardiovasc. procedures; ) • disease prevalence (lung cancer) • Identification of beneficiaries with certain clinical conditions • Geographic variations in treatment/outcome patterns • Volume-outcome studies

33. Websites of interest • ResDAC – Research Data Assistance Center http://www.resdac.umn.edu/ • CMS -- Center for Medicare and Medicaid Services www.cms.gov

34. AN APPLICATION USING MEDICARE ENROLLMENT AND CLAIMS FILES

35. Colorectal cancer screening in the Medicare fee-for-service population: does spillover from managed care matter? Koroukian SM, Litaker DL, Dor A, Cooper G. Medical Care 2005 May;43(5):445-52

36. Background • Health care access and service use may vary according to the level of managed care activity (MCA) in an area, affecting the care received by the uninsured and those with other forms of insurance coverage. •  Medicare expenditures in fee-for-service (FFS) beneficiaries are lower in high MCA areas (Baker L)  SPILLOVER EFFECT

37. Background, cont’d • The degree to which managed care influences preventive service delivery is unclear  implications for the success of screening and early detection programs, especially among higher risk groups.

38. Background, cont’d • Managed care programs have favored receipt of preventive services. This is evidenced through the services covered in the benefits package. • the Medicare program began reimbursement in January 1998 for screening fecal occult blood testing (FOBT) and flexible sigmoidoscopy (FLEX) in average risk beneficiaries and screening colonoscopy (COL) in high-risk individuals.

39. Study Objective • To study colorectal cancer (CRC) screening among Medicare fee-for-service (FFS) beneficiaries in relation to the level of MCA in their county of residence.

40. Depiction of the spillover effect FFS beneficiaries Managed care enrollees

41. Conceptual Framework • Individual-level characteristics: • Age • Race • Sex COLORECTAL CANCER SCREENING • County-level socioeconomic attributes: • Poverty • Education ? REIMBURSEMENT POLICY • County-level Health Systems Characteristics: • Availability of physician resources: • Primary Care Physicians (PCP) • Specialists • Proportion of PCP’s to total physician workforce • MANAGED CARE ACTIVITY

42. Methods • Cross-sectional study using: • 1999 Medicare Denominator file • 1999 Outpatient Standard Analytic File (SAF) • 1999 Part B Physician/Supplier SAF • 1998 Area Resource File

43. Area Resource File (ARF) • County-level characteristics • Socioeconomic attributes • Poverty • Education • Physician resources • Primary Care physicians or PCP’s (per 100,000 residents) • Gastroenterologists (per 100,000 residents) • Percent PCP’s to total physicians

44. Main Outcome Measures • Screening for colorectal cancer by one of three screening modalities: • Fecal occult blood test (FOBT) • Flexible sigmoidoscopy (FLEX) • Colonoscopy (COL) • We also accounted for patients undergoing screening tests through more than one modality • Colonoscopy only (COL-ONLY) • Colonoscopy following FOBT and/or FLEX (COL-WFF)

45. Algorithm to identify colorectal cancer screening procedures: A MULTI-STEP APPROACH Identify colorectal procedures (FOBT, Flex, COL) Surveillance test YES Procedure performed in the presence of symptoms*? Procedure performed in the presence of diagnostic codes indicating the presence of personal or family history of colon cancer? NO YES Screening test Diagnostic test NO * For example, abdominal pain; bleeding; change in bowel habits…

46. Covariates: • Main covariate: Managed Care Activity (MCA) at the county-level, derived from total months of insurance under managed care (MC) and FFS as follows: • MCA categorized as: • Low: MCA < 10% • Moderate: MCA= 10-30% • High: MCA > 30% • MCA = MC months / (MC months + FFS months)

47. Other Covariates • Individual-level: • Age (5 age groups: 65-69; 70-74; 75-79; 80-84; 85+) • Race (Caucasian; Afr-American (AA); Other) • Sex • County-level: • % individuals age 65+ with incomes at or below 100% of the federal poverty level • % adults with high school diploma • Availability of physician resources

48. Analysis • The analytic files were structured as follows: • At the individual level: one record per individual, including demographics, and dichotomous variables indicating whether the individual underwent screening procedure (by modality) • At the county level: one record per county, with socio-demographic attributes (poverty, education); physician resource availability; and MCA

49. Analysis, cont’d • Testing bivariate associations using chi-square • Multi-level logistic models to assess the likelihood of undergoing CRC screening procedure given the MCA level, after controlling for individual- and county-level characteristics • Multi-level models made it possible to account for clustering of screening activities within counties • Used the individual as the unit of analysis • Software packages: SAS 8.12, HLM, ArcView

50. PERCENTILE DISTRIBUTION OF HMO PENETRATION RATE (unit of analysis = county)