Urologic Diseases in America
Available Datasets
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
Defining Burden of Illness
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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
Types of UDA datasets
• Nationally-representative
• Claims-based
• Special populations
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
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
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
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
Healthcare Cost and Utilization Project
(HCUP)
Nationwide Inpatient Sample (NIS)
• Maintained by the Agency for Healthcare 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
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
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)
Some topics that can be illuminated
by HCUP
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Access to care
Complications of care
Surgical volume/outcome relationships
Diffusion of technologies
Practice pattern variation
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
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
National Ambulatory Medical
Care Survey (NAMCS)
• Maintained by the National Center for Health Statistics
• Nationally representative sample of physician office
visits
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Unit of analysis is the visit
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Sample of patient visits is characterized during a
1-week survey period
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 4week survey period
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
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 nonurologists
• Practice pattern variations
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
Surveillance, Epidemiology, End
Results Database
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
Strengths and Limitations
• Strengths :
– Only comprehensive source of populationbased data on cancer stage at diagnosis as well
as cancer mortality
• Limitations:
– Limited follow up data
– VA participation?
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
Strengths and Limitations
Strengths
• Representative data on a vulnerable
population
• Many GU conditions in the elderly
Limitations
• No longitudinal data
• Little clinical detail
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
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
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
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
UDA datasets: Special populations
Special populations
National Association of Children’s Hospitals
and Related Institutions
Society of Assisted Reproductive Technology
database
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
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
UDA Datasets: Claims-based
Centers for Medicare and Medicaid Services
Marketscan
Ingenix
Innovus/I3 database
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
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
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.
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
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 nationallyrepresentative
• Many GU conditions not well represented
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)
Innovus i3 database
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