SOCRATES: SOCial Risk and diAbetes ouTcomEs Study

苏格拉底：社会风险和糖尿病结果研究

• 批准号: 10666513
• 负责人: Seth A Berkowitz
• 金额: $ 59.06万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10666513
• 关键词: Accounting; Address; Adopted; Affect; Age; Area; Blood Pressure; Caring; Categories; Characteristics; Clinic; Clinical; Communities; Complement; Complex; Complications of Diabetes Mellitus; Data; Data Element; Data Set; Data Sources; Diabetes Mellitus; Diastolic blood pressure; Dimensions; Disparity; Disparity population; Economic Conditions; Effectiveness; Ethnic Origin; Food; Frequencies; Future; Gender; Glycosylated hemoglobin A; Health; Health Personnel; Individual; Intervention; Interview; Investigation; Knowledge; LDL Cholesterol Lipoproteins; Learning; Link; Location; Low-Density Lipoproteins; Machine Learning; Methods; Microvascular Dysfunction; Mission; Modeling; National Institute of Diabetes and Digestive and Kidney Diseases; Neighborhood Health Center; Neighborhoods; Non-Insulin-Dependent Diabetes Mellitus; Outcome; Outcome Study; Pathway interactions; Patients; Pattern; Persons; Population; Proliferating; Public Health Informatics; Qualitative Evaluations; Qualitative Research; Race; Regression Analysis; Reporting; Research; Resources; Risk; Services; Social Environment; Social Work; Socioeconomic Status; Structure; Surveys; System; Target Populations; Testing; Transportation; Variant; Vulnerable Populations; Work; cholesterol control; data resource; design; diabetes management; effective intervention; food insecurity; health equity; health management; high risk; housing instability; improved; improved outcome; innovation; knowledge base; local economy; machine learning method; novel; population health; programs; referral services; response; screening; social

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.

抽象的 与健康有关的社会需求，尤其是粮食不安全，住房不稳定和运输障碍，是 与2型糖尿病（T2DM）患者的结局不佳有关。特别是，这些因素是 与较差的血糖，血压和LDL胆固醇控制相关，这显着增加了 大血管和微血管并发症的风险。他们也是种族/种族的主要驱动力 基于社会经济状况的糖尿病结果差异。那，越来越多的呼吁干预 满足这些需求并改善T2DM结果。但是，很少的研究评估了是否改变 与健康相关的社会需求与T2DM结果的变化有关。 这些知识差距阻碍了制定有效干预措施的努力。解决它将提供很多 - 需要证据，以筛选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T2DM的结果。连接社会需求和T2DM的途径 结果的特征是个人（例如需求，年龄，性别，种族/种族）之间的相互作用， 诊所（例如，诊所特征；干预措施的细节）和面积水平的因素（例如，地方经济 状况;社区资源）。鉴于这种复杂性，需要一种多管齐的方法。我们将 结合了3种投资方法：1）纵向，多层次，回归分析； 2）创新机器 学习方法来检测与健康的异质反应相关因素的新型组合 相关的社会需求和与健康相关的社会需求干预措施，同时避免了虚假发现； 3） 周到的定性调查。这样的分析从来没有做过，因为需要 数据元素尚未团结在一起。 该建议旨在回答特定与健康相关的社会需求的改善是否是 与特定临床结果的改善，在什么情况下以及哪种方法相关的 满足与健康相关的社会需求（如果有的话）最好的改善结果。我们将利用我们相信的 该国最大的与患者报告的与健康相关的社会需求，临床成果和社区 - 和诊所级数据。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A1C，收缩压和舒张压以及LDL胆固醇的变化。此外，我们会的 评估基于诊所的干预措施是否改善了这些结果，以及是否有重要的结果 这些干预措施的变化与对干预的反应不同。提议 工作将产生有关如何完善和改善与健康相关的社会的重要证据 需要干预措施。具体来说，它将帮助我们更好地了解如何更好地照顾高风险的人群 对于T2DM并发症。总体而言，该项目将大大提高NIDDK的使命来改善健康状况 T2DM的弱势个人的股权。