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

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 4 health plans from April – October 2009. Outcomes reported are all-cause emergency room (ER) visits and fractures. One retrospective data extract was completed (1/2002–6/2008), followed by seven prospective monthly extracts (1/2008–11/2009). 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 6,586 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 ER 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; 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.

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