Empirical estimation of under-reporting in the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS)

不良事件报告系统 医学 食品药品监督管理局 药品 不利影响 回廊的 急诊医学 内科学 医疗急救 药理学
作者
Yasser Alatawi,Richard A Hansen
出处
期刊:Expert Opinion on Drug Safety [Taylor & Francis]
卷期号:16 (7): 761-767 被引量:106
标识
DOI:10.1080/14740338.2017.1323867
摘要

Background: To examine how closely reporting rates in the FDA Adverse Event Reporting System (FAERS) reflect expected rates of known adverse drug events (ADEs).Methods: We selected three groups of drugs to reflect hypothesized variation in sensitivity to reporting, including statins, biologics, and narrow therapeutics index drugs (NTI). The numbers of ADEs in FAERS were divided by utilization estimates from ambulatory health care data (NAMCS/NHAMCS) to calculate a reported proportion. One sample z-test for proportions compared the proportion of ADEs reported to an expected ADE proportion derived from drug labels, reference databases, and peer-reviewed papers.Results: The majority of drug-ADE pairs showed significant under-reporting. For example, roughly 0.01% to 44% of statin events were reported (z-test p < 0.0001). Biological (0.002% to >100%) and NTI (20% to >100%) drugs had relatively higher reporting rates. Roughly 20% to 33% of the minimum number of expected serious events were reported with biologics and NTI drugs.Conclusions: This study supports previous evidence of under-reporting of ADEs in spontaneous reporting data. But, under-reporting varies considerably by the type of drug and the type of ADEs, and this variability in under-reporting should be considered when interpreting safety signals.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
高LL发布了新的文献求助10
1秒前
ChenYX完成签到,获得积分10
2秒前
科研通AI6.2应助李振聪采纳,获得10
2秒前
顾矜应助李振聪采纳,获得10
2秒前
一把过发布了新的文献求助10
2秒前
兮颜完成签到,获得积分10
3秒前
科目三应助李振聪采纳,获得10
3秒前
完美世界应助李振聪采纳,获得10
3秒前
英俊的铭应助李振聪采纳,获得10
3秒前
JamesPei应助李振聪采纳,获得10
3秒前
3秒前
科研通AI6.4应助李振聪采纳,获得10
3秒前
CipherSage应助TCB采纳,获得10
3秒前
科研通AI6.1应助李振聪采纳,获得10
3秒前
Saunak发布了新的文献求助10
4秒前
4秒前
jiangyi完成签到,获得积分10
5秒前
5秒前
6秒前
FashionBoy应助迷你的笑白采纳,获得10
6秒前
xx完成签到 ,获得积分10
6秒前
ZXY发布了新的文献求助10
9秒前
MQRR发布了新的文献求助10
10秒前
发发发发布了新的文献求助10
10秒前
cxf发布了新的文献求助10
10秒前
Orange应助李振聪采纳,获得10
10秒前
科目三应助李振聪采纳,获得10
10秒前
大个应助李振聪采纳,获得10
10秒前
彭于晏应助李振聪采纳,获得10
10秒前
乐乐应助李振聪采纳,获得10
10秒前
科研通AI6.1应助李振聪采纳,获得10
10秒前
我是老大应助李振聪采纳,获得10
11秒前
华仔应助李振聪采纳,获得10
11秒前
领导范儿应助李振聪采纳,获得10
11秒前
CodeCraft应助李振聪采纳,获得10
11秒前
科研通AI6.1应助Theta采纳,获得10
11秒前
迅速采梦发布了新的文献求助10
12秒前
满意的天完成签到 ,获得积分10
13秒前
13秒前
annian完成签到 ,获得积分10
14秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Chemistry and Physics of Carbon Volume 18 800
The Organometallic Chemistry of the Transition Metals 800
The formation of Australian attitudes towards China, 1918-1941 640
Signals, Systems, and Signal Processing 610
全相对论原子结构与含时波包动力学的理论研究--清华大学 500
Elevating Next Generation Genomic Science and Technology using Machine Learning in the Healthcare Industry Applied Machine Learning for IoT and Data Analytics 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6443580
求助须知:如何正确求助?哪些是违规求助? 8257418
关于积分的说明 17586894
捐赠科研通 5502274
什么是DOI,文献DOI怎么找? 2900939
邀请新用户注册赠送积分活动 1877987
关于科研通互助平台的介绍 1717534