宾夕法尼亚州乳腺癌查模式的多层次预测研究,2021-2022年
1College of Medicine, Pennsylvania State University, Hershey, Pennsylvania, USA.
Journal of primary care & community health
|October 30, 2025
概括
社会决定因素显著影响乳腺癌查的坚持. 机器学习识别了区域剥夺指数 (ADI) 等地缘社会因素,以提高风险女性的查率.
科学领域:
- 公共卫生 公共卫生
- 健康差异 在健康上的差异
- 医疗保健中的机器学习
背景情况:
- 健康的社会决定因素 (SDOH) 是影响癌症查遵守的关键因素.
- 了解地缘社会风险因素对于有针对性的干预至关重要.
- 机器学习为分析复杂的健康数据提供了新的方法.
研究的目的:
- 确定与乳腺癌查不遵守相关的个人,系统和地缘社会风险因素.
- 评估机器学习技术在提高癌症查率方面的有效性.
主要方法:
- 分析了来自宾夕法尼亚州15家家庭医学诊所的21543名50-74岁女性队列 (2021-2022年).
- 地理社会数据被地理编码为人口普查区块,并结合了社区的社会经济措施.
- 多重物流回归和机器学习衍生的区域剥夺指数 (ADI) 用于评估坚持预测指标.
主要成果:
- 少数民族妇女,不包括西班牙裔个人,显示查的几率下降 (aOR:0.659).
- 没有保险的人 (aOR:0.334) 和医疗补助接收者 (aOR:0.793) 的不保险风险更高.
- 区域贫困指数 (ADI) 与乳腺癌查率成反比例.
结论:
- 健康的社会决定因素显著影响乳腺癌查的可能性.
- 识别和解决复合障碍对于提高查遵守率至关重要.
- 机器学习辅助地缘社会因素的分析可以为公共卫生战略提供信息.
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