Davis Matthew, Simpson Kit, Diaz Vanessa, Alekseyenko Alexander V
Biomedical Informatics Center, Department of Public Health Sciences, College of Medicine, Medical University of South Carolina, Charleston, SC 29403, USA.
Department of Public Health Sciences, College of Medicine, Medical University of South Carolina, Charleston, SC 29403, USA.
Fortune J Health Sci. 2024 Mar;7(1):128-137. doi: 10.26502/fjhs.171. Epub 2024 Feb 17.
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.
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.
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.
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.
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.
本研究的目的是描述乳腺癌筛查接受障碍的模式，最终目标是提高筛查依从性并减轻乳腺癌导致的死亡负担。本研究着眼于健康的社会决定因素及其与筛查和死亡率的关联。它还调查了基于县数据训练的模型对个体的可推广程度。
将县级筛查接受率和经年龄调整的乳腺癌死亡率与疾病控制中心的社会脆弱性指数（SVI）相结合，以训练一个预测筛查接受率的模型。然后将学到的模式应用于个体患者的去识别电子病历，以预测乳房X光检查的后续情况。
利用SVI可以对一个县的乳腺癌筛查接受情况做出准确预测。然而，在少数族裔居民比例较高的地区，筛查增加与经年龄调整的死亡率降低之间的关联并不成立。研究还表明，从县SVI数据中学到的模式在患者层面几乎没有鉴别力。
本研究表明，SVI中的社会决定因素可以解释县乳腺癌筛查率的大部分差异。然而，这些相同的模式无法区分哪些患者会及时进行乳房X光筛查。本研究还得出结论，在少数族裔比例较高的地区，筛查增加与经年龄调整的死亡率降低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模式及其与女性乳腺癌筛查接受情况、经年龄调整的乳腺癌死亡率的关联，以及基于县数据训练的模型对个体的可推广程度。
