Research Opportunities Using Hospital Discharge Data

							Use of Large Databases for Research

    Reaping the benefits of your tax dollars

                Jeff Coben, MD
               December 12, 2007
         Learning Objectives
• Understand the strengths and limitations of
  using existing large databases for research
• Gain exposure to the Healthcare Cost and
  Utilization Project databases through a
  review of several examples of prior research
• Understand the process of accessing,
  obtaining, and analyzing existing large
  databases
   Federal Investment in Research
             Databases
• National or regional in scope
• Rigorous and well-defined sampling and/or
  data collection methodologies
• Often longitudinal, with ongoing data
  collection using standardized instruments
• Public domain, with “wrap-around” services
• Surveys, administrative data, census of
  providers
            Common Examples
• Behavioral Risk Factor     • National Health Interview
  Surveillance Survey          Survey
• National Survey on Drug    • National Hospital
  Use and Health               Discharge Survey
• Healthcare Cost and        • National Health and
  Utilization Project          Nutrition Examination
• National Survey of Child     Surveys
  and Adolescent Well-       • National Vital Statistics
  Being                        System
          Federal Investment
• Federal intramural research staff are
  devoted to maintaining these databases
• Intramural staff also increasingly involved
  in database dissemination activities
  Reasons for Using Large Federal
      Databases for Research
• National scope
• Can study trends over time
• Large sample sizes permit sub-analyses and
  multivariate analyses
• Can obtain population-based estimates of
  disease
• COST & EFFICIENCY
       Research Process
     Choosing the research question
                     
         Developing the protocol
                     
   Pre-testing and revising the protocol
                     
          Carrying out the study
                     
          Analyzing the findings
                     
Drawing and disseminating the conclusions
          Research Question
• Do children with traumatic brain injury
  (TBI) benefit from “aggressive” intensive
  care management?
  Management of Pediatric TBI
• TBI is a leading cause of death among
  children
• Variation in the management of critically ill
  TBI patients
• Concerns over costs of aggressive
  management
Research Question - Do children with
traumatic brain injury (TBI) benefit from
“aggressive” intensive care management?


          Develop the Protocol

     1.    Operationalize terms
     2.    Study Design
     3.    Subjects
     4.    Variables (Predictor/Outcome)
     5.    Statistical Issues
      Protocol Development
• TBI case definition = severe brain injury
  requiring endotracheal intubation and
  mechanical ventilation
• Aggressive management = insertion of
  intracranial pressure (ICP) monitor
• Study Design = randomized trial
Study Design = RCT

           Child meets inclusion criteria

 ICU management                  ICU management + ICP



                     Outcomes
                     1. Mortality
                     2. Morbidity
                     3. Costs
    Problems with RCT Design
• Ethical?
• Number of cases needed for prospective
  study (multi-site)
• Time required to enroll sufficient sample
• Cost of the study
     Using Secondary Analysis
• Secondary analysis is the reanalysis of data
  collected by another researcher or
  organization
• The shortcut
      Research Process
      Choosing the research question
                    
         Developing the protocol
                    
   Pre-testing & revising the protocol
                                   Secondary data analysis
         Carrying out the study
                    
          Analyzing the findings
                    
Drawing and disseminating the conclusions
 Variation in therapy and outcome for
    pediatric head trauma patients
        Tilford JM, et al. Crit Care Med 2005

• Study examined the incidence, use of
  procedures, and outcomes of critically ill
  children with TBI between 1988-1999 to
  describe the benefits of improved treatment
• Hypothesis: more aggressive treatment (ICP
  monitoring) over time is associated with
  improved survival
                 Methods
• Used the Nationwide Inpatient Sample
  database to identify all children 0-21 with
  TBI requiring endotracheal intubation
• Used ICD-9-CM codes to identify use of
  ICP monitoring, calculate injury severity
  scores, and describe consciousness level
      Changes in ICP Monitoring and
          Outcome: 1988-1999




                   Injury Severity Score



1988-1989-1990-1991-1992-1993-1994-1995-1996-1997-1998-1999
         Secondary Analysis
• Advantages: Speed and economy
• Disadvantages:
  – No control over data variables
  – Compatibility between the available data and
    the research question
      Compatibility Challenge
• Since data already collected, can’t specify
  what you want
• May require some modification of the
  original research question – or….
• May need to work backwards
• Compatible with the researcher?
           Primary Data Collection

Research Question          Develop Protocol
                           1. Design
                           2. Subjects
                           3. Measures
                           4. Instruments


           Secondary Data Analysis

 Data Source                  Research Questions
 1. Design                    What questions could
 2. Subjects                  these data answer?
 3. Measures
 4. Instruments
  Finding Research Questions to
    Fit an Existing Data Base
• Become familiar with the data content
• Identify pairs or groups of variables whose
  association may be of interest
• Review the literature to determine if these
  research questions are novel and important
• Formulate specific hypotheses and
  statistical methods
• Analyze the data
        HEALTHCARE COST AND UTILIZATION PROJECT




A Family of Databases, Tools and Products
      Understanding Hospital
         Discharge Data
• Hospitals create “discharge abstracts” on
  every patient seen
• Original purpose was billing/reimbursement
• Includes valuable information (>100
  variables)
  – Patient demographics
  – Diagnoses, procedures, complications
  – Charges, length of stay, ICU days
      Hospital Discharge Data
• Individual discharge abstracts are
  computerized
• State regulatory agencies require all
  hospitals to submit all discharge abstracts
  on a regular basis
• Edit checks routinely performed, quality
  assurance, penalties for non-compliance
HEALTHCARE COST AND UTILIZATION PROJECT




 Partners Providing Data
HEALTHCARE COST AND UTILIZATION PROJECT




        HCUP Process




       HCUP Uniform Data
HEALTHCARE COST AND UTILIZATION PROJECT


         State Inpatient
        Databases (SID)



           Uniform
        Comprehensive
       hospital discharge
              data




                     HCUP Uniform Data
  State Inpatient Database (SID)
• Complete data from 37 states
• 90% of all hospital discharges in U.S.
  (N>30 million)
• Example of research using the SID
  – Characteristics of motorcycle-related
    hospitalizations: Comparing states with
    different helmet laws
     • Coben, Steiner, and Miller. Accident Analysis &
       Prevention, 2007
Abstract
This study compares U.S. motorcycle-related hospitalizations
across states with differing helmet laws. Cross-sectional analyses
of hospital discharge data from 33 states participating in the
Healthcare Cost and Utilization Project in 2001 were conducted.
Results revealed that motorcyclists hospitalized from states
without universal helmet laws are more likely to die during the
hospitalization, sustain severe traumatic brain injury, be
discharged to long-term care facilities, and lack private health
insurance. This study further illustrates and substantiates the
increased burden of hospitalization and long-term care seen in
states that lack universal motorcycle helmet use laws.
                   HEALTHCARE COST AND UTILIZATION PROJECT


                           State Inpatient
                           Databases (SID)
Nationwide Inpatient
    Sample (NIS)
• Sample of community         Uniform
  hospitals from SID       Comprehensive
• Approximates 20%        hospital discharge
  sample of community            data
  hospitals in the U.S.




                                       HCUP Uniform Data
 Nationwide Inpatient Sample (NIS)
• Stratified sample of 994 hospitals from the 37
  states contributing data to HCUP (N>7 million)
• Designed for national and regional estimates
• Example of research using the NIS
   – Rural-urban Differences in Injury Hospitalizations
      • Coben, Tiesman, Bossarte, and Furbee (in progress)
    Unadjusted Injury Hospitalization for Selected Causes of Injury by Urbanicity, U.S. 2004



Cause of Injury                          Age Adjusted Rate per 100,000 population (95% CI)
                        Large Urban            Small Urban          Large Rural            Small Rural


Unintentional         455.6 (403.3-515)      439.9 (375-513)     536.2 (470.3-611)    550.3 (471.0-643.9)
  Fall                242.5 (218.0-269.)     225.4 (194-261)     261.5 (230.3-297)     240.3 (209.8-275.7)
  Motor Vehicle       87.7 (74.9-102.7)      87.7 (71.9-106.9)   114.9 (96.9-136.5)    135.1 (108.8-167.8)
  Poisoning            25.6 (22.2-29.6)      24.6 (19.9-30.2)     26.9 (21.8-33.2)       23.5 (18.1-30.4)
Self-inflicted         40.9 (36.3-46.1)      49.2 (42.1-57.4)     63.0 (54.8-72.6)       51.5 (42.7-62.3)
  Poisoning            37.0 (32.9-41.8)      44.9 (38.5-52.4)     57.7 (49.9-66.7)       46.1 (38.2-55.9)

  Cut/pierce             1.8 (1.4-2.3)         1.9 (1.3-2.7)       2.6 (1.8-4.1)          1.8 (0.9-3.8)

  Firearm              34.6 (30.7-39.1)      41.7 (35.7-48.6)     53.7 (46.4-62.2)       42.9 (35.4-52.1)
Assault                34.3 (29.5-39.9)      24.2 (17.9-32.5)     20.5 (16.1-26.4)       19.5 (14.2-26.9)
  Struck by/against    11.3 (9.7-13.2)         8.6 (6.5-11.4)      7.5 (5.6-10.2)         7.3 (5.1-10.7)
  Firearm               8.8 (7.5-10.3)         4.0 (2.6-6.2)       2.9 (1.9-4.8)          2.5 (1.4-4.7)

  Cut/pierce             7.6 (6.3-9.1)         5.8 (4.2-8.1)       4.7 (3.4-6.8)          4.6 (3.1-7.2)
Undetermined            9.4 (8.0-11.1)         8.7 (6.9-10.9)     12.4 (9.7-16.0)        10.6 (7.8-14.6)
                   HEALTHCARE COST AND UTILIZATION PROJECT


                           State Inpatient
                           Databases (SID)
Nationwide Inpatient                            Kids’ Inpatient
    Sample (NIS)                                Data Base (KID)
• Sample of community         Uniform           • Sample of pediatric
  hospitals from SID                              discharges from
                           Comprehensive
                                                  community hospitals
• Approximates 20%        hospital discharge      in the SID
  sample of community            data
  hospitals in the U.S.




                                       HCUP Uniform Data
 Kids’ Inpatient Database (KID)
• Stratified sample of pediatric discharges
  from the SID (N=3 million)
• Allows national and regional studies of
  inpatient hospital utilization and charges for
  children and adolescents
• Example of research using the KID
  – National estimates of ATV injury
    hospitalizations in Children
     • Killingsworth JB, et al. Pediatrics, 2005
                    HEALTHCARE COST AND UTILIZATION PROJECT



                            State Inpatient
                            Databases (SID)
Nationwide Inpatient                             Kids’ Inpatient
    Sample (NIS)                                 Data Base (KID)
• Sample of community                            • Sample of pediatric
  hospitals from SID        Comprehensive          discharges from
                                                   community hospitals
• Approximates 20%         hospital discharge
                                                   in the SID
  sample of community       data from states
  hospitals in the U.S.




 State Outpatient
 Databases (SOD)
• State Ambulatory
  Surgery Data (SASD)
• State Emergency
                                        HCUP Uniform Data
  Department Data
  (SEDD)
State Ambulatory Surgery Databases
             (SASD)
• Ambulatory surgery data provided by 19
  states
• Example of research using SASD
  – The Impact of Endometrial Ablation on
    Hysterectomy Rates in Women with Benign
    Uterine Conditions in the United States
     • Farquhar CM, et al. 2002
    State Emergency Department
         Databases (SEDD)
• Statewide ED data from 17 states
• Example of research using SEDD
  – Hospital and Demographic Influences on the
    Disposition of Transient Ischemic Attack
     • Coben, Owens, Steiner, and Crocco. Academic
       Emergency Medicine, in press.
Objective: Determine factors responsible for the variation in Emergency
Department disposition of TIA cases.

Methods: All ED-treated TIA cases from hospitals in eleven states were
identified from the Healthcare Cost and Utilization Project. Descriptive analyses
compared admitted and discharged cases. Based on the results of the bivariate
analyses, logistic regression models of the likelihood of hospital admission were
derived, using a stepwise selection process. Adjusted risk ratios and 95%
confidence intervals were calculated from the logistic regression models.

Results: A total of 34,843 cases were identified in the 11 states, with 53% of
cases admitted to the hospital. In logistic regression models differences in
admission status were found to be strongly associated with clinical
characteristics such as age and co-morbidities. After controlling for co-
morbidities, differences in admission status were also found to be associated
hospital type and with socio-demographic characteristics, including county of
residence and insurance status.

Conclusions: While clinical factors predictably and appropriately impact the ED
disposition of patients diagnosed with TIA, several non-clinical factors are also
associated with differences in disposition.
                HEALTHCARE COST AND UTILIZATION PROJECT

              State Inpatient       State Ambulatory
              Databases (SID)      Surgery Data (SASD)

AHRQ



                            Central
                           Distributor


                                         Data Use
Public                                   Agreement
Researchers




       CD-SID                                        NIS
                     CD-SASD             KID
                          HEALTHCARE COST AND UTILIZATION PROJECT


             HCUP Tools                             HCUP Research Products




HCUPnet: An interactive, on-line query tool       Products include:
for HCUP data
                                                  Research Studies
Clinical Classification Software
(CCS): Clinical grouper of ICD-9-CM and
ICD-10 codes                                      Statistics and
                                                  Fact Books on
AHRQ Quality Indicators: Measures of              HCUP Data
health care quality based on hospital inpatient
data
Comorbidity Software: Identifies
comorbidities in hospital discharge records
using ICD-9-CM codes and DRGs
     Secondary Analysis of Large
      Research Databases Can…
• Be used to test specific hypotheses
  – Improved outcomes with ICP monitoring
• Be used for descriptive, epidemiological
  studies
  – Large (faculty): Firearm-related hospitalizations
  – Small (students): Rotavirus admissions
• Generate pilot data for future investigations
  – ED prospective study on TIA disposition
         Steps in the Process
• Determine interest area
• Search for existing databases
• Learn the database
  – Data documentation manuals, CDs, web
• Derive research question(s)
• Conduct analyses
  – Statistical consultation, programming
              Additional Tips
•   Contact intramural staff for advice
•   Be thorough with literature searches
•   Understand the limitations of the database
•   Find other publications using the database
Questions/Comments?