Finding and Using Publicly Available Datasets for Secondary Data Analysis Research

1. Finding and Using Publicly Available Datasets for Secondary Data Analysis Research KL2 Seminar February 2011

2. Disclosures and acknowledgements Disclosures: None Acknowledgements: Alex Smith, Michael McWilliams, Ann Nattinger, SGIM Research Committee

3. Two shout-outs • Comparative Effectiveness Research through CTSI Smith AK et al, JGIM 2011

4. 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

5. (My) Definition of Secondary Data Data that have been collected but not for you

6. 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

7. Key Conceptual Issues • Someone else’s 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

8. 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

9. 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

10. 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.

11. 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

14. Get Acquainted with Basics

16. Find a Dataset, Assess Merit & Feasibility

20. CARDIA

21. CARDIA

22. Get Expert Help

23. 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”

24. Eric – Getting Down to Business • Identify datasets relevant to his research interests • Identify health statistics, validated instruments, funding sources • www.sgim.org/go/datasets

26. 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

27. CELDAC • Comparative Effectiveness Large Dataset Analysis Core • UCSF CTSI • Access to local and national datasets and expertise http://ctsi.ucsf.edu/research/celdac

28. 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

29. ISPOR • International Society for Pharmacoepidemiology and Outcomes Research http://www.ispor.org/DigestOfIntDB/CountryList.aspx

30. Inter-University Consortium for Political and Social Research (ICPSR) • World’s 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/index.jsp

31. 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

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

33. 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 • >40 labs • Pharmacy data • All inpatient and outpatient fills • Academic affiliation • etc

34. Administrative Data (VA) • Huge bureaucracy and paperwork

35. Administrative Data (VA) • Messy data • Huge size • 2 TB server • Data analyst

36. Survey Data (NHANES) • National Health and Nutrition Examination Survey (NHANES) • Nationally representative sample of >10K patients every 2 years • Extensive interview data on clinical history (including diseases, behaviors, psychosocial parameters, etc.) • Physical exam information (e.g. VS) • Labs, biomarkers

37. 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

38. 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

40. Survey Data (NAMCS) • Data more from physician perspective (diagnoses, treatments Rx’ed, 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

41. 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

42. 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