Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA.
Pharmacoepidemiol Drug Saf. 2013 May;22(5):488-95. doi: 10.1002/pds.3412. Epub 2013 Feb 12.
This study describes practical considerations for implementation of near real-time medical product safety surveillance in a distributed health data network.
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.
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.
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.
本研究描述了在分布式健康数据网络中实施实时医疗产品安全性监测的实际考虑因素。
我们在 2009 年 4 月至 10 月期间在四个健康计划中进行了试点主动安全性监测，比较了普通丙戊酸钠与历史品牌产品的安全性。报告的结果是所有原因急诊就诊和骨折。完成了一个回顾性数据提取（2002 年 1 月至 2008 年 6 月），随后进行了七个前瞻性月度提取（2008 年 1 月至 2009 年 11 月）。为了评估索赔处理的延迟，我们使用了三种分析方法：实时序贯分析、1.5 个月延迟的序贯分析和非序贯（使用最终回顾性数据）。序贯分析使用最大化序贯概率比检验。记录了主动监测的程序和后勤障碍。
我们确定了 6586 名普通丙戊酸钠的新使用者和 43960 名品牌产品的新使用者。质量控制方法确定了 16 个提取错误，这些错误已得到纠正。实时提取捕获了 87.5%的急诊就诊和 50.0%的骨折，使用 1.5 个月的延迟后，这两个比例分别提高到 98.3%和 68.7%。无论提取时间框架如何，我们都没有发现任何结果的信号，并且在测试统计数据和相对风险估计值方面存在微小差异。
实时序贯安全性监测是可行的，但有几个障碍需要注意。每个数据提取都需要进行数据质量审查。虽然分析延迟不会影响信号检测，但当使用历史对照组时，暴露和结果之间的差异累积可能会从理论上使实时风险估计值偏向于零，从而导致无法检测到信号。
