Finding and Using Publicly Available Datasets for Secondary Data Analysis Research

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Finding and Using Publicly Available Datasets for
Secondary Data Analysis Research

• KL2 Seminar
• February 2011

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Disclosures and acknowledgements

• Disclosures
• None
• Acknowledgements
• Alex Smith, Michael McWilliams, Ann Nattinger,
  SGIM Research Committee

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Two shout-outs

• Comparative Effectiveness Research through CTSI

Smith AK et al, JGIM 2011
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Learning objectives

• Appreciate key conceptual and practical issues
  involved in secondary data analysis
• Identify and use online tools for locating and
  learning about publicly available datasets
  relevant to your research
• Focus on what is useful to you

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(My) Definition of Secondary Data

• Data that have been collected
• but not for you

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Types of Secondary Data

• Survey (NHIS, NHANES, HRS, BRFSS)
• Administrative (Medicare claims)
• Discharge (HCUP SID and NIS)
• Medical chart / EMR
• Disease registries (SEER)
• Aggregate (ARF, US Census)
• Research databases (SOF)
• Combinations and linkages

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Key Conceptual Issues

• Someone elses secondary data is your primary
  data
• Treat data and research plan with same rigor as
  would for a primary data collection study
• Research questions should be conceptually driven,
  interesting a priori
• Some exceptions Warren Browner rule
• Know data as well as if you had collected it
  yourself
• Who is in the cohort?
• Strengths and limitations of data collection
  procedures, instruments

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Selecting a Database

• Compatibility with research question(s)
• Availability and expense
• Sample representativeness, power
• Measures of interest present and valid
• Messiness and missingness
• Local expertise
• Linkages

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Resources Needed

• Your effort
• Computer resources and security
• Programmer and/or statistician effort
• PhD statistical support complex sampling or
  analyses
• Coordinator if merging datasets
• Realistic timeline / Gantt chart

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Cases

• Amita is a junior faculty member interested in
  doing a secondary data analysis project on
  association between race/ethnicity and the
  prevalence and outcomes of atrial fibrillation.
  No prior experience and limited direct
  mentorship.
• Eric is a junior faculty member with past
  experience. Wants to find new dataset around
  which write grant on association between SES and
  ADL function in elders.

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Amita Getting Started

• Amita
• Get acquainted with basics
• Find dataset and assess merit and feasibility
• Find a mentor / get expert help
• www.sgim.org/go/datasets

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Get Acquainted with Basics
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Find a Dataset, Assess Merit Feasibility
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CARDIA
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CARDIA
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Get Expert Help
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Getting Expert Help

• Request a consultation
• 1 on 1 consultation
• Clear, defined questions about dataset
• strengths and weaknesses about using XYZ to
  study patterns of medication use for heart
  failure

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Eric Getting Down to Business

• Identify datasets relevant to his research
  interests
• Identify health statistics, validated
  instruments, funding sources
• www.sgim.org/go/datasets

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Finding Additional Resources

• National Information Center on Health Services
  Research and Health Care Technology (NICHSR)
• Inter-University Consortium for Political and
  Social Research (ICPSR)
• Partners in Information Access for the Public
  Health Workforce
• Roadmap K-12 Data Resource Center (UCSF)
• List of datasets from the American Sociologic
  Association
• Canadian Research Data Centers Data Sets and
  Research Tools (Canada)
• Directory of Health and Human Services Data
  Resources
• Publicly Available Databases from National
  Institute on Aging (NIA)
• Publicly Available Databases from National Heart,
  Lung, Blood Institute (NHLBI)
• National Center for Health Statistics (NCHS) Data
  Warehouse
• Medicare Research Data Assistance Center
  (RESDAC) and Centers for Medicare and Medicaid
  Services (CMS) Research, Statistics, Data
  Systems
• Veterans Affairs (VA) data

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CELDAC

• Comparative Effectiveness Large Dataset Analysis
  Core
• UCSF CTSI
• Access to local and national datasets and
  expertise

http//ctsi.ucsf.edu/research/celdac
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National Information Center on Health Services
Research and Health Care Technology (NICHSR)

• Databases, data repositories, health statistics
• Fellowship and funding opportunities
• Glossaries, research and clinical guidelines
• Evidence-based practice and health technology
  assessment
• Specialized PubMed searches on healthcare quality
  and costs

http//www.nlm.nih.gov/hsrinfo/index.html
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ISPOR

• International Society for Pharmacoepidemiology
  and Outcomes Research

http//www.ispor.org/DigestOfIntDB/CountryList.asp
x
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Inter-University Consortium for Political and
Social Research (ICPSR)

• Worlds largest archive of social science data
• Searchable
• Many sub-archives relevant to HSR
• Health and Medical Care Archive
• National Archive of Computerized Data on Aging

http//www.icpsr.umich.edu/icpsrweb/ICPSR/access/i
ndex.jsp
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Questions?

• Specific high-value datasets
• Causal inference / comparative effectiveness
• Which comes first RQ or dataset?
• Evaluating and managing validity of measures
• Analyzing complex survey data

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EXTRA SLIDES

• Additional brief information about specific
  high-value datasets
• VA administrative data
• NHANES
• NAMCS
• NIS

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Administrative Data (VA)

• VA has multiple high-value administrative
  databases
• Outpatient visit information
• Visit date, type of clinic, provider, ICD9
  diagnoses
• Inpatient information
• Admitting dx(s), discharge dx(s), CPT codes, bed
  section, meds administered
• Lab data
• gt40 labs
• Pharmacy data
• All inpatient and outpatient fills
• Academic affiliation
• etc

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Administrative Data (VA)

• Huge bureaucracy and paperwork

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Administrative Data (VA)

• Messy data
• Huge size
• 2 TB server
• Data analyst

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Survey Data (NHANES)

• National Health and Nutrition Examination Survey
  (NHANES)
• Nationally representative sample of gt10K patients
  every 2 years
• Extensive interview data on clinical history
  (including diseases, behaviors, psychosocial
  parameters, etc.)
• Physical exam information (e.g. VS)
• Labs, biomarkers

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Survey Data (NHANES)

• Free and easy to download
• (Relatively) easy to use
• Although requires careful reading of
  documentation
• Serial cross-sectional
• Disease data self-report
• Very limited information about providers and
  systems of care

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Survey Data (NAMCS)

• National Ambulatory Medical Care Survey (NAMCS)
  and National Hospital Ambulatory Medical Care
  Survey (NHAMCS)
• Nationally representative sample of 70K
  outpatient and ED visits per year
• Physician-completed form about office visit

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Survey Data (NAMCS)

• Data more from physician perspective (diagnoses,
  treatments Rxed, etc) and some info on providers
  (e.g., clinic organization, use of EMRs, etc)
• Serial cross-sectional
• Visit-focused
• Not comprehensive, ? value for chronic diseases

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Discharge Data (NIS)

• National Inpatient Sample (NIS)
• Database of inpatient hospital stays collected
  from 20 of US community hospitals by AHRQ
• Diagnoses and procedures, severity adjustment
  elements, payment source, hospital organizational
  characteristics
• Hospital and county identifiers that allow
  linkage to the American Hospital Association
  Annual Survey and Area Resource File

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Discharge Data (NIS)

• Relatively easy to access (DUA, 200/yr)
• Relatively easy to use
• Though need close attention to documentation
• Limited data elements
• Huge data files