评估所有美国研究计划中流行率估计的概括性
Barrett Wallace Montgomery1, Mahmoud Elkasabi1, M Daniel Brannock1
1RTI International, Durham, North Carolina, United States.
American journal of epidemiology
|January 15, 2026
概括
权重方法可以改善来自我们所有人痴呆症和2型糖尿病研究计划的疾病患病率估计. 然而,这些技术可能会增加对抑郁症患病率的偏见,强调需要仔细分析电子健康记录数据.
科学领域:
- 流行病学 流行病学
- 精准医学是一门精准的医学.
- 医疗信息学 医疗信息学
背景情况:
- 美国国立卫生研究院的"我们所有人"研究计划收集来自不同人群的多式联络数据,以推进精准医学.
- "我们所有人"的非概率抽样可能会限制与美国一般人口相比,流行率估计的概括性.
- 从电子健康记录 (EHR) 中估计疾病患病率存在独特的挑战,特别是在代表性不足的群体中.
研究的目的:
- 从我们所有人的数据集中评估基于电子健康记录的疾病患病率的挑战.
- 为解决可通用性问题提供一个框架和一个R包 (waou).
- 评估权衡技术的有效性,以改善痴呆症,2型糖尿病和抑郁症的流行率估计.
主要方法:
- 研究了三种加权技术,以提高疾病流行率估计的概括性.
- 利用了来自我们所有人的研究计划的数据.
- 采用国家健康访谈调查作为比较的基准.
主要成果:
- 权重方法改善了痴呆症和2型糖尿病的流行率估计的代表性.
- 权衡技术在抑郁症的流行率估计中放大了偏差.
- 该R包"waou"是为了帮助研究人员应用和评估这些方法而开发的.
结论:
- 权重可以提高对某些疾病的"我们所有人"流行率估计的概括性,但可能会对其他疾病产生偏差.
- "waou" R包为研究人员提供了一个工具,以批判性地评估他们的发现的概括性.
- 在使用"我们所有人"数据集进行人口级流行病学推断时,仔细考虑潜在的偏见至关重要.
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