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使用监督机器学习和PROGRESS-Plus框架对自我报告的乳腺癌查出席不平等的交叉分析

Núria Pedrós Barnils¹, Benjamin Schüz¹

¹Institute for Public Health and Nursing Research, University of Bremen, Bremen, Germany.

Frontiers in public health

|January 22, 2024

概括

在西班牙,乳腺癌查 (BCS) 的出席率不平等. 这项研究确定了特定的妇女群体,考虑到地区和教育等因素,她们不参加BCS的风险更高.

科学领域:

公共卫生公共卫生
流行病学流行病学
健康差异在健康上的差异

背景情况:

乳腺癌查 (BCS) 对西班牙的公共卫生至关重要,自20世纪90年代以来,该计划一直在运行.
尽管每两年邀请45-69岁的女性参加,但在不同的人口群体之间仍然存在显著的出席不平等.
本研究使用了定量交叉方法来识别因缺席而面临风险的群体.

研究的目的:

确定与西班牙乳腺癌查出席率较低相关的交叉位置.
了解社会和人口因素如何交叉影响BCS吸收.
为改善BCS覆盖率提供有针对性的干预信息.

主要方法:

利用了西班牙2020年欧洲健康访谈调查 (N=22,072) 的数据.
应用了来自PROGRESS-Plus框架的不平等指标.
使用决策树模型 (CARTs,CHAID,CITs,C5.0) 和集成算法 (XGBoost) 来识别交叉组.

主要成果:

XGBoost认为地区差异是BCS参加的主要因素,其次是教育,年龄和婚姻状况.
C5.0模型表明,个体特征的重要性因地区和相互作用而异.
不出席的风险最高的是文盲,老年人,在西班牙出生,居住在特定地区的较低社会阶层的妇女,以及已婚/离婚/寡妇妇女.

结论:

决策树和整体算法是识别公共卫生资源未充分利用风险人群的有效工具.
这些发现可以指导开发有针对性的干预措施,以增加乳腺癌查出席率.
解决跨界不平等问题是改善公平获得癌症查计划的关键.

关键词:

普罗格雷斯+的框架是什么?西班牙西班牙西班牙西班牙西班牙乳腺癌查乳腺癌查决策树决策树是一个决定树.在健康方面的不平等.获得医疗保健的机会.跨学科的跨学科性监督机器学习是指监督机器学习.

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