Urologic Diseases in America

1. Urologic Diseases in America Available Datasets

2. Urologic Diseases in America Mission: 1. Define the burden of illness posed on the nation by the major urologic conditions 2. Use existing data to inform public policy, identify promising areas for new research, identify existing health care quality problems

3. Defining Burden of Illness • Prevalence and incidence • Inpatient stays • Hospital outpatient visits • Physician office visits • Ambulatory surgery visits • Emergency room visits • Nursing home admissions • Direct costs (national and Medicare) • Indirect costs

4. Types of UDA datasets • Nationally-representative • Claims-based • Special populations

5. UDA Datasets Nationally representative datasets: Healthcare Cost and Utilization Project- Nationwide Inpatient Sample National Ambulatory Medical Care Survey National Hospital Ambulatory Medical Care Survey National Survey of Ambulatory Surgery Surveillance, Epidemiology, and End Results National Health and Nutrition Examination Survey Medical Expenditure Panel Survey National Nursing Home Survey

6. National Health and Nutrition Examination Survey (NHANES) • Maintained by the National Center for Health Statistics • First released as NHANES I, II, III • Now released every two years • Population-based survey of households • Mobile Examination Center allows physical and laboratory data collection after household interview

7. National Health and Nutrition Examination Survey (NHANES) • In-person interview provides comprehensive sociodemographic, dietary and medical history • Each survey has a few ‘urology’ questions (EDUrinary Incontinence and BPH) • Comprehensive labs done • DEXA scanning, audiology, etc

8. Strengths and Limitations Strengths : • Clinically detailed, nationally-representative data • Ability to describe minority health issues • Environmental exposures • Possible link to other datasets Limitations: • No longitudinal data • Limited scope of urologic conditions

9. Healthcare Cost and Utilization Project (HCUP)Nationwide Inpatient Sample (NIS) • Maintained by the Agency forHealthcare Quality and Research • Nationally representative data on hospital inpatient stays (20% stratified sample of hospitals in the US) • Unit of analysis is the hospital discharge • http://hcupnet.ahrq.gov/ • Can be linked to AHA and Area Resource File databases

10. HCUP-NIS • Largest collection of longitudinal hospital care data in the United States • Can be used to identify, track, and analyze national trends in access, charges, quality • The only national hospital database with charge information on all patient stays, regardless of payer

11. HCUP-NIS • 6-7 million stay records (37 states represented) • Over 100 variables, including Primary and secondary diagnoses Primary and secondary procedures Admission and discharge status Patient demographics Expected payment source Total charges Length of stay Hospital characteristics (e.g., ownership, size, teaching status)

12. Some topics that can be illuminated by HCUP • Access to care • Complications of care • Surgical volume/outcome relationships • Diffusion of technologies • Practice pattern variation

13. Strengths and Limitations Strengths • Large sample, ability to describe inpatient procedure experience for many GU conditions • Population-based • Charge data Limitations • No longitudinal data • ICD-9 procedure coding only • Charge data

14. Kids’ Inpatient Database (KID) • HCUP-NIS for pediatric discharges • Nationally representative sample of peds discharges (2-3 million discharges) • Conducted 1997, 2000, 2003 • Strengths and Limitations similar to NIS

15. National Ambulatory Medical Care Survey (NAMCS) • Maintained by the National Center for Health Statistics • Nationally representative sample of physician office visits • Unit of analysis is the visit • Sample of patient visits is characterized during a 1-week survey period

16. National Hospital Ambulatory Medical Care Survey (NHAMCS) • Maintained by the national center for health statistics • Nationally-representative sample of ambulatory care services in hospital emergency and outpatient departments • Unit of analysis is the visit • Each patient visit is characterized during a 4-week survey period

17. NHAMCS and NAMCS Variables recorded include • age, sex, race, ethnicity • patients’ symptoms, complaints or other reasons for the visit • physicians’ diagnoses • diagnostic and therapeutic services • medications • expected sources of payment • visit disposition

18. Some topics that can be illuminated by NAMCS/NHAMCS • Use of physician services for GU conditions by race and gender • Medication practice patterns • Treatment of GU conditions by non-urologists • Practice pattern variations

19. Strengths and Limitations Strengths • Captures physician subspecialties that may encounter urologic conditions • Large, nationally representative portrait of outpatient care, for all types of insurance Limitations • Limited data on procedures (ICD-9 coding) and testing • No longitiudinal data • Often required combining cells across demographic strata or years to achieve adequate counts

20. Surveillance, Epidemiology, End Results Database

21. Surveillance, Epidemiology, End Results Database (SEER) • Maintained by National Cancer Institute and Centers for Disease Control • Covers about 26% of the population • SEER population is somewhat more urban and foreign-born than the general population • Collects patient demographics, tumor site, histology, stage, initial treatment, vital status

22. Strengths and Limitations • Strengths : • Only comprehensive source of population-based data on cancer stage at diagnosis as well as cancer mortality • Limitations: • Limited follow up data • VA participation?

23. National Nursing Home Survey (NNHS) • Maintained by National Center for Health Statistics • National sample surveys of nursing homes, the providers of care, and their residents • Sample size: • 1,500 facilities • 8,100 residents • Information is provided on the recipients of care, including demographics, health status, and services received • 1995. 1997, 1999, 2004

24. Strengths and Limitations Strengths • Representative data on a vulnerable population • Many GU conditions in the elderly Limitations • No longitudinal data • Little clinical detail

25. Medical Expenditure Panel Survey (MEPS) • Source: Agency for Healthcare Research and Quality • Nationally representative survey of health care use, expenditures, sources of payment, and insurance coverage for the US civilian non-institutionalized population • Provides information on the financing and utilization of medical care in the United States • Sample size: 10,000 families (or 24,000 individuals) • Survey is continuous, population-based

26. MEPS MEPS “household interview” components: • health conditions, health status, use of medical services, charges and source of payments, access to care, satisfaction with care, health insurance coverage, income, and employment • Followed up by confirmation/supplementation from providers, employers, insurers

27. Strengths and Limitations Strengths • Outpatient prescription drug expenditures • Detailed and reliable expenditure data Limitations • Conditions identified at the 3-digit ICD-9 level • Small sample to detect many GU conditions

28. National Survey of Ambulatory Surgery • Nationally-representative data regarding freestanding and hospital-based ambulatory surgery centers • ICD-9 diagnosis and procedure codes • Data only from 1994-96 • HCUP has a State Ambulatory Surgery Database with only hospital-based surgeries

29. UDA datasets: Special populations Special populations National Association of Children’s Hospitals and Related Institutions Society of Assisted Reproductive Technology database

30. National Association of Children’s Hospitals and Related Institutions (NACHRI) database • NACHRI dataset contains information on all inpatient stays at 58 member hospitals, including approximately 2 million pediatric inpatient discharges • Variables of interest: diagnosis, demographics, length of stay, total charges, and cost-to-charge ratio • Limited detail for substantive analyses • 1999- onward

31. Society for Assisted Reproductive Technologies (SART) database • SART is a professional society which collects data from fertility clinics across the nation, in concert with CDC • Demographics, outcomes, indications for ART use • 1999 data • Access is by request

32. UDA Datasets: Claims-based Centers for Medicare and Medicaid Services Marketscan Ingenix Innovus/I3 database

33. Centers for Medicare and Medicaid Services (CMS) • Inpatient Stays/ Medicare Provider Analysis and Review(MedPAR) (5% sample) Contains claims for Medicare beneficiaries using hospital inpatient services • Outpatient Hospital Claims (5% sample) Contains claims for Medicare beneficiaries using hospital outpatient services • Physician/Supplier Part B (5% sample) Contains claims for Medicare beneficiaries using physician services

34. Strengths and Limitations Strengths • Enormous database describing healthcare utilization for vast majority of Americans 65 and over • Common Procedural Terminology (CPT) codes • Detailed expenditure data • Ability to follow individuals over time Limitations • Lack of clinical detail • Only captures those who receive care • Lack of outpatient medication information • Excludes those in HMOs

35. SEER-Medicare linkage • Linkage available for 1991-2002 incident cases to 2005 claims (2006 update coming) • Links clinical data from SEER (stage, grade) with utilization data from CMS • Data in house on renal, bladder, and prostate cancers • Specific permission must be obtained from NCI for each analysis.

36. Strengths vs Limitations Strengths • Ability to combine clinical detail from SEER with longitudinal utilization data from Medicare • Look at costs, disparities in care, variations in care, technology diffusion Limitations • Limited to the cancer experience of the elderly • No quality of life data

37. MarketScan • Dataset of claims from 100 health plans serving Fortune 500 employers • Enables evaluation of productivity and pharmacy data and associated medical claims information • Unique source of indirect cost data • Patients’ experience may not be nationally-representative • Many GU conditions not well represented

38. Ingenix • Includes 1.8 million enrolled employees and their dependents • Provides detailed financial information, such as procedure and diagnosis codes and plan costs • Copays, deductibles included • Not nationally-representative • Used in first UDA project to model incremental costs associated with a diagnosis (controls for age, sex, zip code median income, plan type, comorbidities)

39. Innovus i3 database

40. Strengths and Limitations Strengths Ability to follow individuals through 5 years 30 million covered lives Unique lab data Limitations Non-representative Lab data are inconsistently reported