与IRIS® (视觉智能研究) 注册表中缺少的社会人口统计数据相关的因素
Connor Ross1, Alexander Ivanov1, Tobias Elze1
1Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.
Ophthalmology science
|August 14, 2024
概括
缺少的社会人口统计数据,特别是种族和民族,在IRIS注册表中普遍存在,因实践特征而有所不同. 解决这些数据缺口对于研究准确性和减少偏见至关重要.
科学领域:
- 眼科医生 眼科 眼科
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 社会人口统计数据对于了解患者群体和识别健康差异至关重要.
- IRIS® (视觉智能研究) 注册表收集真实世界的数据,但可能有缺失的信息.
- 识别缺失数据的模式是提高数据质量的关键.
研究的目的:
- 为了确定IRIS注册表中缺少社会人口统计数据的普遍性.
- 为了确定与不完整的患者数据相关的实践层面因素.
- 为提高数据完整性和减少研究偏见的策略提供信息.
主要方法:
- 一项横截面研究分析了来自3306个IRIS注册实践中的66 477 365名患者的电子健康记录.
- 计算了包括种族,种族和保险类型在内的变量缺失数据的流行率.
- 多变量线性回归研究了实践特征 (大小,位置,访问频率) 和缺失数据之间的关联.
主要成果:
- 对于种族 (24.8%),种族 (30.2%) 和保险类型 (17.0%) 观察到显著的缺失.
- 缺失数据的流行显示了地理和时间趋势.
- 缺少的种族数据与较少的患者访问,非私人保险患者的比例较高,以及城市实践地点相关.
结论:
- 在IRIS注册表中存在缺失的种族,种族和保险数据的地理和时间变化.
- 实践特征,如大小,位置和患者人口统计学影响数据的完整性.
- 需要标准化的方法来最大限度地减少缺失的数据,并确保研究可重复性.
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