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 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

• 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

SampleNational Ambulatory Medical Care SurveyNational Hospital Ambulatory Medical Care SurveyNational Survey of Ambulatory Surgery Surveillance, Epidemiology, and End Results National Health and Nutrition Examination SurveyMedical Expenditure Panel SurveyNational 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

(EDUrinary 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 demographicsExpected payment source Total charges Length of stay Hospital characteristics (e.g., ownership, size, teaching status)

Some topics that can be illuminated by HCUP

• 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 dataLimitations• 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

• Unit of analysis is the visit

• 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 4-week survey period

NHAMCS and NAMCS

Variables recorded include age, sex, race, ethnicity patients’ symptoms, complaints or other reasons

for the visitphysicians’ diagnosesdiagnostic and therapeutic services medications expected sources of paymentvisit 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 non-urologists

• Practice pattern variations

Strengths and LimitationsStrengths

• 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 population-

based 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 timeLimitations• 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 nationally-representative

• 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