Get access

Near real-time adverse drug reaction surveillance within population-based health networks: methodology considerations for data accrual

Authors

  • Taliser R. Avery,

    Corresponding author
    1. Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
    • The HMO Research Network Center for Education and Research in Therapeutics, Boston, MA, USA
    Search for more papers by this author
  • Martin Kulldorff,

    1. Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
    2. The HMO Research Network Center for Education and Research in Therapeutics, Boston, MA, USA
    Search for more papers by this author
  • Yury Vilk,

    1. Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
    Search for more papers by this author
  • Lingling Li,

    1. Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
    Search for more papers by this author
  • T. Craig Cheetham,

    1. The HMO Research Network Center for Education and Research in Therapeutics, Boston, MA, USA
    2. Pharmacy Analytical Service, Kaiser Permanente, Downey, CA, USA
    Search for more papers by this author
  • Sascha Dublin,

    1. The HMO Research Network Center for Education and Research in Therapeutics, Boston, MA, USA
    2. Group Health Research Institute, Seattle, WA, USA
    Search for more papers by this author
  • Robert L. Davis,

    1. The HMO Research Network Center for Education and Research in Therapeutics, Boston, MA, USA
    2. Center for Health Research, Kaiser Permanente Georgia, Atlanta, GA, USA
    Search for more papers by this author
  • Liyan Liu,

    1. The HMO Research Network Center for Education and Research in Therapeutics, Boston, MA, USA
    2. Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
    Search for more papers by this author
  • Lisa Herrinton,

    1. The HMO Research Network Center for Education and Research in Therapeutics, Boston, MA, USA
    2. Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
    Search for more papers by this author
  • Jeffrey S. Brown

    1. Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
    2. The HMO Research Network Center for Education and Research in Therapeutics, Boston, MA, USA
    Search for more papers by this author

  • Prior presentation: These data have not previously been presented.

Correspondence to: T. R. Avery, Department of Population Medicine, Harvard Medical School and HPHC Institute, 133 Brookline Avenue, 6th Floor, Boston, MA 02215, USA. E-mail: Taliser_Avery@hphc.org

ABSTRACT

Purpose

This study describes practical considerations for implementation of near real-time medical product safety surveillance in a distributed health data network.

Methods

We conducted pilot active safety surveillance comparing generic divalproex sodium to historical branded product at four health plans from April to October 2009. Outcomes reported are all-cause emergency room visits and fractures. One retrospective data extract was completed (January 2002–June 2008), followed by seven prospective monthly extracts (January 2008–November 2009). To evaluate delays in claims processing, we used three analytic approaches: near real-time sequential analysis, sequential analysis with 1.5 month delay, and nonsequential (using final retrospective data). Sequential analyses used the maximized sequential probability ratio test. Procedural and logistical barriers to active surveillance were documented.

Results

We identified 6586 new users of generic divalproex sodium and 43 960 new users of the branded product. Quality control methods identified 16 extract errors, which were corrected. Near real-time extracts captured 87.5% of emergency room visits and 50.0% of fractures, which improved to 98.3% and 68.7% respectively with 1.5 month delay. We did not identify signals for either outcome regardless of extract timeframe, and slight differences in the test statistic and relative risk estimates were found.

Conclusions

Near real-time sequential safety surveillance is feasible, but several barriers warrant attention. Data quality review of each data extract was necessary. Although signal detection was not affected by delay in analysis, when using a historical control group differential accrual between exposure and outcomes may theoretically bias near real-time risk estimates towards the null, causing failure to detect a signal. Copyright © 2013 John Wiley & Sons, Ltd.

Get access to the full text of this article

Ancillary