确定缺失乳腺癌查风险的交叉组:比较基于回归和决策树的方法
Núria Pedrós Barnils1, Benjamin Schüz1
1Institute for Public Health and Nursing Research, University of Bremen, Bremen, Germany.
SSM - population health
|January 6, 2025
概括
识别那些没有参加乳腺癌查 (BCS) 的风险较高的妇女对于改善参与至关重要. 一种决策树方法确定了特定群体,包括某些地区的寡妇妇女,因为不参与BCS计划而面临更高的风险.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 乳腺恶性瘤是德国妇女死亡的主要原因.
- 全国范围内的乳腺癌查 (BCS) 计划旨在早期检测,但面临参与率和社会人口不平等的挑战.
- 鉴定非参与的高风险群体是复杂的,因为缺点交叉.
研究的目的:
- 确定在德国不参加BCS的风险较高的女性交叉组.
- 为了比较基于证据的回归策略与基于决策树的回归策略的有效性来识别这些群体.
主要方法:
- 来自德国2019年欧洲健康访谈调查 (N=23,001) 的数据分析.
- 开发了两个后勤回归模型:一个是基于证据的,一个是基于决策树的 (分类和回归树).
- 两种模型都使用基于PROGRESS-Plus特征的交叉分类交叉组,根据年龄进行调整.
主要成果:
- 基于证据的方法确定了低收入的女性出生在德国以外,生活在农村,并没有同居的高风险 (OR=9.48).
- 决策树方法确定了在特定联邦州独自生活或与依赖者生活的寡妇妇女,作为高风险 (OR=3.43).
- 决策树模型显示了更高的歧视性准确性 (AUC=0.6726对0.6618) 并确定了细微的风险群体.
结论:
- 基于决策树的回归提供了更高的准确性,用于识别因不参与乳腺癌查而面临风险的交叉组.
- 这种方法可以揭示研究不足的人群,并为有针对性的干预措施提供信息,以改善BCS吸收并减少健康不平等.
- 调查结果强调,需要考虑复杂的社会维度,而不仅仅是单一的不平等因素,以制定有效的公共卫生战略.
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