Abstract
Research in obstetrics and gynecology (OB/GYN) increasingly relies on "big data" and observational study designs. There is a gap in practitioner-relevant guides to interpret and critique such research. This guide is an introduction to interpreting research using observational data and provides explanations and context for related terminology. In addition, it serves as a guide for critiquing OB/GYN studies that use observational data by outlining how to assess common pitfalls of experimental and observational study designs. Lastly, the piece provides a compendium of observational data resources commonly used within OB/GYN research. Review of literature was conducted for the collection of definitions and examples of terminology related to observational data research. Data resources were collected via Web search and researcher recommendations. Next, each data resource was reviewed and analyzed for content and accessibility. Contents of data resources were organized into summary tables and matched to relevant literature examples. We identified 26 observational data resources frequently used in secondary analysis for OB/GYN research. Cost, accessibility considerations for software/hardware capabilities, and contents of each data resource varied substantially. Observational data sources can provide researchers with a variety of options in tackling their research questions related to OB/GYN practice, patient health outcomes, trends in utilization of medications/procedures, or prevalence estimates of disease states. Insurance claims data resources are useful for population-level prevalence estimates and utilization trends, whereas electronic health record-derived data and patient survey data may be more useful for exploring patient behaviors and trends in practice.
Highlights
Importance: Research in obstetrics and gynecology (OB/GYN) increasingly relies on “big data” and observational study designs
We identified 26 observational data resources frequently used in secondary analysis for OB/GYN research
Insurance claims data resources are useful for population-level prevalence estimates and utilization trends, whereas electronic health record–derived data and patient survey data may be more useful for exploring patient behaviors and trends in practice
Summary
Terminology and definitions relevant to observational data research were identified via literature and keyword searches for terms using PubMed/MEDLINE and Google Scholar. Examples of the relevant identified terminology were based on reviews of the literature in OB/ GYN research–focused journals. Additional Web searches were conducted to identify data resources, and the identified government-sponsored data collection agencies were cross-referenced to find further data resources. Researcher recommendations were used to gather observational data sources, along with review of current literature for observational studies or secondary data analysis in OB/GYN-focused journals. The contents of each data resource were summarized, and the cost and accessibility of each resource were verified with the data provider or government data collection agency. Distance to treatment facilities was calculated and compared using nonparametric testing for counties by rural and Appalachian status
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