{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T20:15:45Z","timestamp":1783455345111,"version":"3.55.0"},"reference-count":27,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2013,2,12]],"date-time":"2013-02-12T00:00:00Z","timestamp":1360627200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pharmacoepidemiology and Drug"],"published-print":{"date-parts":[[2013,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>This study describes practical considerations for implementation of near real\u2010time medical product safety surveillance in a distributed health data network.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>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\u2010cause emergency room visits and fractures. One retrospective data extract was completed (January 2002\u2013June 2008), followed by seven prospective monthly extracts (January 2008\u2013November 2009). To evaluate delays in claims processing, we used three analytic approaches: near real\u2010time sequential analysis, sequential analysis with 1.5\u2009month 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We identified 6586 new users of generic divalproex sodium and 43\u2009960 new users of the branded product. Quality control methods identified 16 extract errors, which were corrected. Near real\u2010time 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\u2009month 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Near real\u2010time 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\u2010time risk estimates towards the null, causing failure to detect a signal. Copyright \u00a9 2013 John Wiley &amp; Sons, Ltd.<\/jats:p><\/jats:sec>","DOI":"10.1002\/pds.3412","type":"journal-article","created":{"date-parts":[[2013,2,12]],"date-time":"2013-02-12T11:41:12Z","timestamp":1360669272000},"page":"488-495","source":"Crossref","is-referenced-by-count":8,"title":["Near real\u2010time adverse drug reaction surveillance within population\u2010based health networks: methodology considerations for data accrual"],"prefix":"10.1002","volume":"22","author":[{"given":"Taliser R.","family":"Avery","sequence":"first","affiliation":[{"name":"Department of Population Medicine Harvard Medical School and Harvard Pilgrim Health Care Institute  Boston MA USA"},{"name":"The HMO Research Network Center for Education and Research in Therapeutics  Boston MA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Kulldorff","sequence":"additional","affiliation":[{"name":"Department of Population Medicine Harvard Medical School and Harvard 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