division of geriatrics finding and using publicly available datasets for secondary data analysis...

Division of Geriatrics

Finding and Using Publicly Available Datasets for Secondary Data

Analysis Research

KL2 SeminarFebruary 2011


Disclosures:

None

Acknowledgements:

Alex Smith, Michael McWilliams, Ann Nattinger, SGIM Research Committee

Disclosures and acknowledgements


Two shout-outs

• Comparative Effectiveness Research through CTSI

Smith AK et al, JGIM 2011


• 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

Learning objectives


Data that have been collected

but not for you

(My) Definition 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

Types of Secondary Data


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


• Compatibility with research question(s)

• Availability and expense

• Sample: representativeness, power

• Measures of interest present and valid

• Messiness and missingness

• Local expertise

• Linkages

Selecting a Database


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


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.


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


Get Acquainted with Basics


Find a Dataset, Assess Merit & Feasibility


CARDIA


Get Expert Help


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”


Eric – Getting Down to Business

• Identify datasets relevant to his research interests

• Identify health statistics, validated instruments, funding sources

• www.sgim.org/go/datasets


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


CELDAC

• Comparative Effectiveness Large Dataset Analysis Core– UCSF CTSI

• Access to local and national datasets and expertise

http://ctsi.ucsf.edu/research/celdac


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


ISPOR

• International Society for Pharmacoepidemiology and Outcomes Research

http://www.ispor.org/DigestOfIntDB/CountryList.aspx


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


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


EXTRA SLIDES

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


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



• Huge bureaucracy and paperwork



• Messy data

• Huge size– 2 TB server

• Data analyst


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


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


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


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


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


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

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