Large Databases – Introduction

1. Large Databases – Introduction

2. Why Large Databases? • Time/cost efficient • Many of them available for public use • Readily accessible • Either population-based (claims data; vital records), or cover representative samples of the population

3. Objectives • Overview of large databases with regard to: • their applications • use and misuse in studies of epidemiology and health services research • discussion of the structure and content of databases that are widely used in the field • review of studies that have used the above databases • logistics of obtaining access to the data • the ethical issues relative to working with such databases • basic techniques for data retrieval and analysis

4. Types of databases • Claims based databases: • Medicare (Disabled, ESRD, Aged) • Medicaid (Low income, Disabled) • Survey Data: • National Health Interview Survey (NHIS), Health and Retirement Study (HRS) • Other population-based databases: • Health Care Cost and Utilization Project (HCUP) • Birth and Death Certificates • Tumor Registries • Linked databases

5. Analytic considerations – Data Source • How, where, when are the data collected • Who collects the data • How are the data compiled • Form in which the data is delivered to analyst for research

6. Analytic considerations – Population • Who is covered in the database • Gain understanding on the nature of the program (Medicare/ Medicaid / VA) • How to identify study cohorts • Implications of how study cohorts are defined • generalizability

7. Analytic considerations – Variables • What is the source of a given variable • Data quality • Accuracy • Completeness • Values (data dictionary) • Different uses/applications

8. Analytic considerations – Other • Strengths • Limitations • Applications

9. ? FISHING EXPEDITION

10. Research must be grounded in a carefully designed conceptual framework [many journals require a detailed description of the conceptual framework]

11. USE OF CONCEPT MAPPING TO DEVELOP CONCEPTUAL FRAMEWORKS (Trochim; Novak) • Concept mapping is a pictorial representation of the thinking process; a tool for organizing and representing knowledge • Concept maps show the framework in which a theory is stated (a postulated relationship between two or more concepts) • How ideas are related to each other • Which ideas are more important/relevant • Conceptual frameworks are used as a guide in planning and evaluation.

12. Steps in the development of a concept map • Develop the focus/domain for the conceptualization • Identify key concepts that apply to this domain • boxes • Representation of statements: identify cross-links – how these domains are related to one another • Arrows  associations, not causality

13. Steps in the development of a concept map, cont’d • Start with the endpoint (dependent variable, outcome) • Identify potential correlates based on empirical or theoretical evidence • Show mediating variables

14. Steps in the development of a concept map, cont’d • Only include concepts that will be operationally defined and measured • Discuss this… • Present left to right or top-to-bottom • VISUALLY PLEASING • Arrows not criss-crossing • Label concepts succinctly • Do not include operational definitions or values of variables in the model

16. CONCEPTUAL FRAMEWORKS ARE ALWAYS EVOLVING!