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发病率和流行率:一个R包,使用OMOP常用数据模型计算人口水平的发病率和流行率
Berta Raventós1,2, Martí Català3, Mike Du3
1Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.
Pharmacoepidemiology and drug safety
|October 25, 2023
概括
发病率和流行率R包可靠地估计疾病发病率和流行率从现实世界的数据映射到观察医疗结果伙伴关系 (OMOP) 共同数据模型 (CDM). 该工具支持可重复的流行病学研究.
科学领域:
- 流行病学 流行病学
- 医疗信息学 医疗信息学
- 软件开发 软件开发
背景情况:
- 现实世界数据 (RWD) 对人口层面的疾病流行病学至关重要.
- 标准化数据格式,如观察医学结果伙伴关系 (OMOP) 共同数据模型 (CDM) 促进大规模分析.
- 准确的发病率和流行指标对于公共卫生监测和研究至关重要.
研究的目的:
- 开发一个名为IncidencePrevalence的强大且易于使用的R包.
- 为了使发病率和流行率 (点和时期) 的计算使用格式为OMOP CDM的RWD.
- 为描述性流行病学研究提供可靠的工具.
主要方法:
- 发病率和流行率R套件被开发并进行了单元测试.
- 通过计算来自西班牙 (SIDIAP) 和英国 (CPRD Aurum) 的RWD的COVID-19发病率来评估面部有效性.
- 通过复制两项已发表的研究,使用来自荷兰 (IPCI) 和英国 (CPRD Gold) 的数据,包括执行时间的基准分析,进一步验证了该包的性能.
主要成果:
- 发病率和流行率与IPCI和CPRD黄金数据库中先前公布的数据高度一致.
- 该软件包在不同数据库中表现良好.
- 计算的COVID-19发病率与最初的流行浪潮后的公共数据保持一致.
结论:
- 发病率流行率R包是使用OMOP CDM格式数据进行描述性流行病学研究的宝贵工具.
- 它可以从大型现实数据集中对发病率和流行率进行可靠和可重复的估计.
- 该包提供了一个简单而又可扩展的分析框架,用于及时进行流行病学估计.
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