1 / 31

Strengths and Weaknesses of Administrative Data in Clinical Research

This article explores the types of research questions that can and cannot be answered using administrative data in clinical research. It discusses the characteristics of administrative data, its strengths and weaknesses, and provides examples of how it has been used in longitudinal and cross-sectional research. The article also highlights the challenges and considerations in working with administrative data.

asarah
Download Presentation

Strengths and Weaknesses of Administrative Data in Clinical Research

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Administrative Data in Clinical Research:Strengths and WeaknessesWilliam D. Leslie, MD MSc FRCPCUniversity of Manitoba

  2. Objectives • To be able to list the kinds of research questions that can (and cannot) be answered from administrative data.

  3. What Are Administrative Data? • Information “for some administrative purpose (e.g., keeping track of the population eligible for certain benefits, paying doctors or hospitals), but not primarily for research or surveillance purposes” • Passive (involuntary) • Under PHIA, does not require consent ***

  4. Hospital Registry Medical Claims DPIN Manitoba Health Data:Health Information Management PHIN Identifiable Personal Health Information

  5. What Are Not Administrative Data? • Information primarily collected for research or surveillance purposes • Clinical registry, patient survey data • Active (voluntary) • Under PHIA, does require consent

  6. Hospital Registry Hospital Registry Medical Claims Medical Claims DPIN DPIN Srcambled PHIN PHIN Manitoba Health Data Manitoba Centre for Health Policy MHHLS Health Information Management

  7. Hospital Registry Medical Claims DPIN Srcambled PHIN Manitoba Health Data Manitoba Centre for Health Policy Anonymized Personal Health Information

  8. Cross-Sectional Research

  9. Cross-Sectional Research

  10. What’s in a Name?

  11. ICD-9-CM Depression • … • 294 Persistent mental disorders classified elsewhere • 295 Schizophrenic disorders • 296 Episodic mood disorders • 297 Delusional disorders • 298 Other nonorganic psychoses • 299 Pervasive developmental disorders • … • 310 Nonpsychotic disorders due to brain damage • 311 Depressive disorder, not elsewhere classified • 312 Disturbance of conduct, not elsewhere classified • …

  12. ICD-9-CM Depression • 296 Episodic mood disorders • 296.0 Bipolar I disorder, single manic episode • 296.1 Manic disorder, recurrent episode • 296.2 Major depressive disorder, single episode • 296.3 Major depressive disorder, recurrent episode • 296.4 Bipolar I disorder, most recent episode manic • 296.5 Bipolar I disorder, most recent episode depressed • 296.6 Bipolar I disorder, most recent episode mixed • 296.7 Bipolar I disorder, most recent episode unspecified • 296.8 Other and unspecified bipolar disorders • 296.9 Other and unspecified episodic mood disorder

  13. ICD-10-CA Depression • F30 Manic episode • F31 Bipolar disorder • F32 Major depressive disorder, single episode • F33 Major depressive disorder, recurrent • F34 Persistent mood [affective] disorders • F39 Unspecified mood [affective] disorder

  14. Longitudinal Research

  15. Longitudinal Research Prior exposure Event Time Case-Control

  16. Longitudinal Research Prior exposure Event Time Case-Control Prior exposure Event Time Historical cohort

  17. Longitudinal Research Prior exposure Event Time Case-Control Prior exposure Event Time Historical cohort … 2000/01 2001/02 2002/03 2003/04 2004/05 … Time Time-trend analysis

  18. Bone Density Hospital Registry Medical Claims Survey DPIN X-ray Lab Scrambled PHIN Nuclear Medicine Cancer Registry DNA Banks MCHP Data Repository:Getting Outside of the Box

  19. Physician claims for bone densitometry vs. clinical database Leslie WD et al. J Clin Densitom. 2005.

  20. Hospital Registry Scanner Data BMD Reports Medical Claims Billing Info Image Files DPIN Srcambled PHIN PHIN Manitoba Health Data Manitoba Centre for Health Policy Manitoba BMD Database (>125,000 records) 100+ publications and a WHO collaboration site

  21. Hospital Registry Scanner Data BMD Reports Medical Claims Billing Info Image Files DPIN Srcambled PHIN Manitoba Health Data Manitoba Centre for Health Policy Manitoba BMD Database (>125,000 records) PHIN Blinded Image Files

  22. Extended Database Research

  23. Practice Change Through System Change:before and after study • 10y fracture risk reporting Jan 1st 2006: • 25% reduction in treatment initiation • 55% reduction in osteopenia treatment Leslie WD. Ann Intern Med 2010

  24. MHHLS Research Support

  25. MHHLS Mail Researcher Team Recruitee Contacts Researcher (maybe) PHIN Registry Hospital Registry Medical Claims DPIN Case Contacts Researcher Case Contact by MHHLS Manitoba Health Research Support:Health Information and Mail Management MHHLS HIM PHIN Case Identification

  26. Manitoba Patient Access NetworkRCT run through MHHLS • Women and men age 50 and older with: • Hip, Spine, Humerus or Colle’s fracture. • 2,901 incident fracture cases randomized (1:1:1) to one of the three groups: • usual care, • mailed physician-only notification or • mailed physician & patient notification. • Assessed: • BMD testing, treatment initiation (DPIN).

  27. Usual Care in Manitoba Women Men Leslie WD. CMAJ 2011.

  28. Post Fracture Care in Manitoba Women Men Care increased 2.4 to 2.8-fold Cost per successful intervention ~$30 Total trial cost ~$40,000 Leslie WD. CMAJ 2011.

  29. Working with administrative data is like drinking from a fire hose…

  30. Summary

More Related