SOCRATES: SOCial Risk and diAbetes ouTcomEs Study

摘要 与健康有关的社会需求，特别是粮食不安全、住房不稳定和交通障碍， 与2型糖尿病（T2 DM）患者的不良结局相关。具体而言，这些因素是 与血糖、血压和低密度脂蛋白胆固醇控制较差相关，这显著增加了 大血管和微血管并发症的风险。他们也是种族/族裔和 社会经济地位在糖尿病结局中的差异。因此，越来越多的人呼吁采取干预措施， 满足这些需求并改善T2 DM结局。然而，很少有研究评估是否有变化， 健康相关的社会需求与T2 DM结局的变化相关。 这种知识差距阻碍了制定有效干预措施的努力。解决这一问题将提供很多- 需要证据来证明哪些患者需要筛查哪些社会需求，以及针对哪些干预措施 社会需求最有可能改善T2 DM的结局。联系社会需求和T2 DM的途径 结果的特征可能在于个体之间的相互作用（例如，需求、年龄、性别、种族/民族）， 诊所-（例如，临床特征;干预的细节），和区域水平因素（例如，地方经济 社区资源（Community Resources）鉴于这种复杂性，需要采取多管齐下的办法。我们将 联合收割机3种调查方法：1）纵向、多层次、回归分析; 2）创新机 学习方法，以检测与对健康的异质反应相关的因素的新组合， 相关的社会需求和健康相关的社会需求干预措施，同时避免虚假的发现;以及3） 深思熟虑的定性调查。这样的分析以前从来没有可能，因为需要 数据元素尚未统一。 这项建议旨在回答改善与健康有关的具体社会需求是否 与特定临床结局的改善相关，在什么情况下，以及 解决与健康有关的社会需求，如果有的话，最能改善结果。我们将利用我们认为是 全国最大的患者报告的健康相关社会需求，临床结果和社区数据集- 临床数据。我们将研究与健康相关的社会需求的变化是否与 血红蛋白A1 c、收缩压和舒张压以及LDL胆固醇的变化。此外，我们将 评估基于临床的干预是否似乎可以改善这些结果，以及是否有重要的 这些干预措施的变化与对干预措施的不同反应有关。拟议 这项工作将产生重要的，以前无法获得的证据，说明如何完善和改善与健康有关的社会 需要干预。具体来说，它将帮助我们更好地了解如何更好地照顾高危人群 2型糖尿病并发症总的来说，该项目将大大推进NIDDK改善健康的使命 为T2 DM患者提供平等的机会。

ABSTRACT Health-related social needs, particularly food insecurity, housing instability, and transportation barriers, are associated with poor outcomes for people with type 2 diabetes mellitus (T2DM). In particular, these factors are associated with worse glycemic, blood pressure, and LDL cholesterol control, which significantly increases the risk of macrovascular and microvascular complications. They are also key drivers of racial/ethnic and socioeconomic status-based disparities in diabetes outcomes. Thus, there is a growing call for interventions to address these needs and improve T2DM outcomes. However little research has assessed whether changes in health-related social needs are associated with changes in T2DM outcomes. This knowledge gap hampers efforts to develop effective interventions. Addressing it will provide much- needed evidence on which patients to screen for which social needs, and on which interventions targeting social needs are most likely to improve T2DM outcomes. The pathways linking social needs and T2DM outcomes are likely characterized by interactions between individual- (e.g., needs, age, gender, race/ethnicity), clinic- (e.g., clinic characteristics; specifics of the intervention), and area-level factors (e.g., local economic conditions; community resources). Given this complexity, a multi-pronged approach is needed. We will combine 3 methods of investigation: 1) longitudinal, multi-level, regression analysis; 2) innovative machine learning methods to detect novel combinations of factors associated with heterogeneous response to health- related social needs and health-related social needs interventions while avoiding spurious findings; and 3) thoughtful qualitative investigation. Such an analysis has never before been possible, because the needed data elements have not been united. This proposal seeks to answer whether improvements in specific health-related social needs are associated with improvements in specific clinical outcomes, in what circumstances, and which approaches to addressing health-related social needs, if any, best improve outcomes. We will leverage what we believe to be the nation’s largest dataset of patient-reported health-related social needs, clinical outcomes, and community- and clinic-level data. We will examine whether changes in health-related social needs are associated with changes in hemoglobin A1c, systolic and diastolic blood pressure, and LDL cholesterol. Further, we will evaluate whether clinic-based interventions seem to improve these outcomes, and if there are important variations in these interventions that are associated with different response to the intervention. The proposed work will yield important, previously unavailable evidence on how to refine and improve health-related social need interventions. Specifically, it will help us understand better how better to care for a population at high risk for T2DM complications. Overall, this project will substantially advance NIDDK’s mission to improve health equity for vulnerable individuals with T2DM.