Near real-time adverse drug reaction surveillance within population-based health networks: methodology considerations for data accrual.
Near real-time adverse drug reaction surveillance within population-based health networks: methodology considerations for data accrual.
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DOI：
10.1002/pds.3412
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发表时间：
2013-05
影响因子：
2.6
通讯作者：
Brown, Jeffrey S.
中科院分区：
文献类型：
--
作者：
Avery, Taliser R.;Kulldorff, Martin;Vilk, Yury;Li, Lingling;Cheetham, T. Craig;Dublin, Sascha;Davis, Robert L.;Liu, Liyan;Herrinton, Lisa;Brown, Jeffrey S.
本研究描述了在分布式健康数据网络中实施近实时医疗产品安全监测的实际考虑。我们在2009年4月至10月的4个健康计划中进行了试点主动安全性监测，比较了仿制药双丙戊酸钠与历史品牌产品。报告的结果是全因急诊室（ER）就诊和骨折。完成了一次回顾性数据提取（1/2002-6/2008），随后进行了7次前瞻性月度提取（1/2008-11/2009）。为了评估索赔处理的延迟，我们使用了三种分析方法：近实时顺序分析，1.5个月延迟的顺序分析和非顺序（使用最终的回顾性数据）。序贯分析采用最大序贯概率比检验。记录了主动监测的程序和后勤障碍。我们确定了6，586名仿制药双丙戊酸钠新用户和43，960名品牌产品新用户。质量控制方法确定了16个提取错误，并进行了纠正。近实时提取物捕获了87.5%的ER访视和50.0%的骨折，延迟1.5个月后分别提高到98.3%和68.7%。我们没有识别出任何一种结果的信号，无论提取时间范围如何;在检验统计量和相对风险估计中发现了轻微的差异。近实时序贯安全性监测是可行的，但有几个障碍值得注意。有必要对每个数据提取物进行数据质量审查。尽管信号检测不受分析延迟的影响，但当使用历史对照组时，暴露和结局之间的差异累积理论上可能使近实时风险估计值偏向零，导致无法检测到信号。
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 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. 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. 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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影响因子：
2.6
作者：
Platt, Richard;Carnahan, Ryan M.;Weiner, Mark G.
通讯作者：
Weiner, Mark G.
影响因子：
2.6
作者：
Brown, Jeffrey S.;Kulldorff, Martin;Platt, Richard
通讯作者：
Platt, Richard
影响因子：
2.6
作者：
Rodriguez, EM;Staffa, JA;Graham, DJ
通讯作者：
Graham, DJ
DOI：
10.1161/circoutcomes.108.801654
发表时间：
2008-11-01
影响因子：
6.9
作者：
Go, Alan S.;Magid, David J.;Gurwitz, Jerry H.
通讯作者：
Gurwitz, Jerry H.
影响因子：
9.9
作者：
Berg, M. J.;Gross, R. A.;Haskins, L. S.
通讯作者：
Haskins, L. S.