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: 10425471
• 负责人: CARRIE R. HOWELL
• 金额: $ 13万
• 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/10425471
• 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; Education; Enrollment; Environment; Ethnic Origin; Exercise; Focus Groups; Food; Future; Glucose; Goals; Health; Health Food; Health Personnel; Health system; Incidence; 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; 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; base; cohort; contextual factors; design; diabetes prevention program; diabetes risk; disorder prevention; disparity reduction; effectiveness evaluation; experience; health data; health disparity; health equity; high risk; improved; income insurance; individualized prevention; innovation; intersectionality; low socioeconomic status; physical inactivity; population based; recruit; risk prediction; risk prediction model; risk stratification; rurality; skills; social; social factors; social health determinants; social integration; social vulnerability; socioeconomic disadvantage

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地位 (SES) 较低的人群，同时对其不利 邻里因素——例如缺乏体育活动资源、有限的健康食品选择、社会经济因素 医疗保健的劣势和障碍——通常与个人社会经济地位较低相交叉，加剧了差距。这 多重影响力（个人、社区、社会）的交叉性导致高度脆弱 患糖尿病的风险较高的人群，并可能导致糖尿病的显着区域差异 糖尿病风险。鉴于美国约有 8800 万成年人患有糖尿病前期，并且大多数结构化 糖尿病预防方法（例如改变生活方式和药物治疗）需要大量时间和金钱 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得到优化 益处。糖尿病风险预测模型可用于识别进展为高风险的个体 糖尿病;然而，传统模型包含临床参数，很少整合社会因素，忽略了 对疾病预防的多层次影响。因此，整合健康的社会决定因素（SDoH） 风险分层的数据有可能根据临床和社会情况识别高风险个体 脆弱性，促进更有针对性的干预和缩小差距。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 成果并解决人口层面的差异。此外，了解如何招募和参与 高风险、社会弱势患者，以及如何个性化预防工作——例如糖尿病 预防计划——有可能改善人口层面的糖尿病结果和健康公平。 因此，本 K01 提案的目标是：(1) 评估将 SDoH 添加到已验证的糖尿病风险中 预测模型——心脏代谢疾病分期（CMDS）——确定风险的改善 分为两个基于人群的队列； (2) 确定成人糖尿病高危人群的患病率， 在 UAB 卫生系统中使用风险分层进行临床和社会方面的评估； (3) 确定策略 通过利益相关者参与，让高风险、社会弱势群体参与糖尿病预防。 进行这项研究，结合提出的培训和指导计划，将帮助我获得 健康差异和 SDoH 测量方面的技能和经验；利益相关者的参与和质量 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能力 研究路径侧重于利用健康的社会决定因素 (SDoH) 数据为更好的设计提供信息 预防心脏代谢疾病的定制举措。这项研究将为信息提供基础 未来的一项试验将根据临床和结果评估实施糖尿病预防计划的有效性 SDoH 因素，最终减少差异。