Harnessing social determinant of health data to identify and engage high risk, socially vulnerable populations for diabetes prevention

利用健康数据的社会决定因素来识别和吸引高风险、社会弱势群体来预防糖尿病

• 批准号: 10653777
• 负责人: CARRIE R. HOWELL
• 金额: $ 12.54万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-26 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10653777
• 关键词: Address; Adult; Affect; Area; Award; Blood Pressure; Body mass index; Cardiometabolic Disease; Characteristics; Cholesterol; Classification; Clinical; Computerized Medical Record; Coronary Artery Risk Development in Young Adults Study; Data; Diabetes Mellitus; Diabetes prevention; Disparity; Education; Enrollment; Environment; Ethnic Origin; Exercise; Focus Groups; Food; Future; Glucose; Goals; Health; Health Food; Health Personnel; Health system; Incidence; Income; Individual; Insurance Coverage; Intervention; Interview; Investments; K-Series Research Career Programs; Life Style Modification; Longitudinal cohort; Measurement; Mentors; Methods; Modeling; Neighborhoods; Non-Insulin-Dependent Diabetes Mellitus; Outcome; Outcome Measure; Patients; Pharmaceutical Preparations; Population; Prediabetes syndrome; Prevalence; Prevention; Prevention approach; Public Health; Qualitative Methods; Race; Reasons for Geographic And Racial Differences in Stroke; Research; Research Project Grants; Resources; Risk; Societies; Socioeconomic Status; Staging; Structure; Time; Training; Validation; Variant; Vulnerable Populations; barrier to care; cohort; contextual factors; design; diabetes prevention program; diabetes risk; disorder prevention; disparity reduction; effectiveness evaluation; experience; health data; health determinants; health disparity; health equity; high risk; high risk population; improved; individualized prevention; innovation; intersectionality; low socioeconomic status; physical inactivity; population based; progression risk; recruit; risk prediction; risk prediction model; risk stratification; rurality; sex; skills; social; social factors; social health determinants; social vulnerability; socioeconomic disadvantage; statistics

ABSTRACT Type 2 diabetes disproportionately affects those with low socioeconomic status (SES), while unfavorable neighborhood factors — such as lack of physical activity resources, limited healthy food options, socioeconomic disadvantage and barriers to health care — often intersect with low individual SES, compounding disparity. This intersectionality of multiple levels of influence (individual, neighborhoods, society) results in highly vulnerable populations at greater risk for developing diabetes, and likely contributes to marked regional variations in diabetes risk. Given that approximately 88 million adults in the U.S. have prediabetes, and most structured diabetes prevention approaches (e.g. lifestyle modification and medications) require significant time and financial investment, efforts that enable health care providers to prioritize the most high-risk patients would optimize benefit. Diabetes risk prediction models can be used to identify individuals at high-risk for progression to diabetes; however, traditional models include clinical parameters with little integration of social factors, ignoring the multiple levels of influence on disease prevention. Thus, integrating social determinants of health (SDoH) data into risk stratification has the potential to identify high-risk individuals based on clinical and social vulnerabilities, facilitating better targeted interventions and reductions in disparities. Importantly, risk stratification approaches that utilize SDoH in the electronic medical record (EMR) may provide an avenue to improve diabetes outcomes and address disparity at the population level. Moreover, understanding how to recruit and engage high-risk, socially vulnerable patients, as well as how to individualize prevention efforts — such as the Diabetes Prevention Program — has potential to improve diabetes outcomes and health equity at the population level. Therefore, the goals of this K01 proposal are to: (1) evaluate the addition of SDoH to a validated diabetes risk prediction model — the cardiometabolic disease staging (CMDS) — to determine improvement of risk classification in two population-based cohorts; (2) determine the prevalence of adults at high-risk for diabetes, both clinically and socially, in the UAB Health System using risk stratification; and (3) identify strategies to engage high-risk, socially vulnerable individuals in diabetes prevention using stakeholder engagement. Conducting this research, in combination with the training and mentoring plan proposed, will help me to obtain skills and experience in health disparities and SDoH measurement; stakeholder engagement and qualitative methods; and diabetes clinical outcome measurement. This award will allow me to develop my independent research path focusing on utilizing social determinants of health (SDoH) data to inform the design of better tailored initiatives for the prevention of cardiometabolic disease. This study will provide the groundwork to inform a future trial to assess the effectiveness of delivering the Diabetes Prevention Program, based on clinical and SDoH factors, to ultimately decrease disparities.

抽象的 2型糖尿病不成比例地影响社会经济状况低的人（SES），而不利 邻里因素 - 例如缺乏体育活动资源，健康食品选择有限，社会经济 缺点和医疗保健的障碍 - 通常与个人SES相交，使差异更加复杂。这 多个影响力的交叉性（个人，社区，社会）导致高度脆弱 患糖尿病风险更大的人群，可能导致明显的区域差异 糖尿病风险。鉴于美国约有8800万成年人有糖尿病前期，最结构化的成年人 糖尿病预防方法（例如生活方式修改和药物）需要大量时间和财务 投资，使医疗保健提供者能够优先考虑最高风险患者的努力将优化 好处。糖尿病风险预测模型可用于识别高风险的个人，以进步 糖尿病;但是，传统模型包括几乎没有社会因素整合的临床参数，忽略了 对预防疾病的多种影响。那是整合健康的社会决定者（SDOH） 风险分层的数据有可能根据临床和社会识别高风险个体 脆弱性，支持更好的针对性干预措施和差异。重要的是，风险分层 在电子病历（EMR）中利用SDOH的方法可能会提供改善糖尿病的途径 成果和解决人口水平的差异。此外，了解如何招募和参与 高风险，社会脆弱的患者以及如何个性化预防努力（例如糖尿病） 预防计划 - 有可能改善人口水平的糖尿病结果和健康公平。 因此，该K01提案的目标是：（1）评估在经过验证的糖尿病风险中添加SDOH 预测模型 - 心脏代谢疾病分期（CMDS） - 确定风险的改善 在两个基于人群的队列中进行分类； （2）确定糖尿病高危成年人的患病率， 在临床和社会上，在UAB卫生系统中使用风险分层； （3）确定策略 使用利益相关者参与，在预防糖尿病中让高风险，社会脆弱的人参与。 进行这项研究，结合提出的培训和心理计划，将有助于我获得 健康分配和SDOH测量方面的技能和经验；利益相关者的参与和定性 方法;和糖尿病临床结果测量。这个奖项将使我能够发展我的独立 研究路径重点是使用健康的社会决定者（SDOH）数据，以告知设计更好的设计 针对预防心脏代谢疾病的量身定制倡议。这项研究将提供基础来告知 一项未来的试验，以根据临床和 SDOH因素，最终降低差异。