测量被动监控系统中的错误分类和样本偏差:改善国家级被动监控系统中关键先天性心脏缺陷的患病率估计
Chris Barnett1, James Christiansen2, Monica Mills1
1Department of Health Division of Public Health Section of Women's, Children's, and Family Health Maternal and Child Health Epidemiology, Anchorage, Alaska, USA.
Birth defects research
|August 1, 2024
概括
被动监控系统中的行政诊断代码错误地分类了关键先天性心脏缺陷 (CCHD),导致偏见的流行率估计. 基于样本的审查策略可以提高心脏病发病率的准确性.
科学领域:
- 公共卫生监督 公共卫生监督
- 儿童心脏病学 儿童心脏病学
- 生物统计学 生物统计学
背景情况:
- 使用行政诊断代码的被动监控系统常用于识别严重先天性心脏缺陷 (CCHD).
- 然而,这些代码用于病例检测和流行率估计的准确性尚未完全理解.
- 错误分类可能会对公共卫生数据和资源分配产生重大影响.
研究的目的:
- 评估通过行政诊断代码识别的12种严重先天性心脏缺陷 (CCHD) 的报告错误分类.
- 测量错误分类对CCHD患病率估计的影响.
- 调查基于样本的审查策略,以估计监督错误分类.
主要方法:
- 从临床上审查了报告的419例中414例CCHD (2007-2018).
- 计算确认概率以评估错误分类和调整流行率估计.
- 采用反复随机抽样策略 (1000次重复) 跨比例 (20%-90%) 评估样本偏差和估计变异性.
主要成果:
- 不同CCHD的错误分类率差异很大,从19%到84%.
- 未经证实的患病率估计有时高于国家数据,而确定的患病率则相似或更低.
- 基于样本的审查表明,有可能改进流行率估计.
结论:
- 使用行政诊断代码对CCHD的被动监测容易导致错误分类,导致偏见的流行率估计.
- 基于样本的审查方法可以提高CCHD的流行率估计的准确性,而不是未经证实的率.
- 这种方法提供了一种可行的策略,以提高对出生缺陷的公共卫生监测数据的可靠性.
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