根据健康的社会决定因素可预测县级乳房 X 线照相术接受率,但对单个患者而言模式并不成立。

Fortune journal of health sciences Pub Date : 2024-03-01 Epub Date: 2024-02-17 DOI:10.26502/fjhs.171
Matthew Davis, Kit Simpson, Vanessa Diaz, Alexander V Alekseyenko
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引用次数: 0

摘要

目的:本研究的目的是描述乳腺癌筛查的障碍模式,最终目标是提高筛查的依从性,减少乳腺癌造成的死亡负担。本研究探讨了健康的社会决定因素及其与筛查和死亡率的关系。研究还探讨了根据县级数据建立的模型在多大程度上可推广到个人:方法:将县级乳腺癌筛查率和年龄调整后死亡率与美国疾病控制中心的社会脆弱性指数(SVI)结合起来,训练一个预测筛查率的模型。然后将学习到的模式应用于患者个人的去身份化电子病历,以预测乳房 X 线照相筛查的后续情况:结果:利用 SVI 可以准确预测一个县的乳腺癌筛查接受率。然而,在少数民族居民比例较高的地区,筛查率的提高与年龄调整死亡率的降低之间的联系并不成立。研究还表明,从县级 SVI 数据中得出的模式在患者层面上几乎不具有鉴别力:本研究表明,SVI 中的社会决定因素可以解释县级乳腺癌筛查率的大部分差异。然而,同样的模式却无法区分哪些患者会及时接受乳房 X 光筛查。本研究还得出结论,在高比例的少数民族地区,增加筛查与降低年龄调整死亡率之间的核心关联并不成立:本研究的目的是描述健康的社会决定因素的模式及其与女性乳腺癌筛查率、年龄调整后乳腺癌死亡率之间的关系,以及根据县级数据建立的模型在多大程度上可以推广到个人。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
County Mammogram Uptake can be Predicted from Social Determinants of Health, but Patterns Do Not Hold for Individual Patients.

Purpose: The objective of this study is to describe patterns in barriers to breast cancer screening uptake with the end goal of improving screening adherence and decreasing the burden of mortality due to breast cancer. This study looks at social determinants of health and their association to screening and mortality. It also investigates the extent that models trained on county data are generalizable to individuals.

Methods: County level screening uptake and age adjusted mortality due to breast cancer are combined with the Centers for Disease Controls Social Vulnerability Index (SVI) to train a model predicting screening uptake rates. Patterns learned are then applied to de-identified electronic medical records from individual patients to make predictions on mammogram screening follow through.

Results: Accurate predictions can be made about a county's breast cancer screening uptake with the SVI. However, the association between increased screening, and decreased age adjusted mortality, doesn't hold in areas with a high proportion of minority residents. It is also shown that patterns learned from county SVI data have little discriminative power at the patient level.

Conclusion: This study demonstrates that social determinants in the SVI can explain much of the variance in county breast cancer screening rates. However, these same patterns fail to discriminate which patients will have timely follow through of a mammogram screening test. This study also concludes that the core association between increased screening and decreased age adjusted mortality does not hold in high proportion minority areas.

Objective: The objective of this study is to describe patterns in social determinants of health and their association with female breast cancer screening uptake, age adjusted breast cancer mortality rate and the extent that models trained on county data are generalizable to individuals.

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