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.

摘要 2型糖尿病对低社会经济地位(SES)的人影响不成比例，尽管对他们不利 邻里因素--如缺乏体力活动资源、健康食品选择有限、社会经济 医疗保健方面的劣势和障碍--往往与个人社会保障水平低交织在一起，加剧了差距。这 多层次影响(个人、社区、社会)的交叉性导致高度脆弱 患糖尿病风险较高的人群，并可能导致明显的地区差异 糖尿病风险。鉴于美国约有8800万成年人患有糖尿病前期，而且大多数是结构性的 预防糖尿病的方法(如改变生活方式和药物治疗)需要大量的时间和资金 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 利益。糖尿病风险预测模型可用于识别进展为 糖尿病；然而，传统的模型包括临床参数，几乎没有整合社会因素，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) 风险分层的数据具有基于临床和社会识别高危个体的潜力 这有助于采取更有针对性的干预措施，减少差距。重要的是，风险分层 在电子病历(EMR)中使用SDoH的方法可能提供一种改善糖尿病的途径 结果和解决人口层面的差距问题。此外，了解如何招聘和参与 高危、易受社会伤害的患者，以及如何使预防工作个体化--如糖尿病 预防计划-有可能改善糖尿病结果和人口层面的健康公平。 因此，K01提案的目标是：(1)评估SDoH添加到有效的糖尿病风险中 预测模型-心脏代谢性疾病分期(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流行率， 在临床和社会方面，在UAB卫生系统中使用风险分层；以及(3)确定战略，以 利用利益相关者的参与，让高危、社会脆弱的个人参与糖尿病预防。 进行这项研究，结合建议的培训和指导计划，将帮助我获得 健康差距和SDoH测量方面的技能和经验；利益相关者参与和定性 方法；和糖尿病临床结局评估。这个奖项将使我能够发展我的独立性 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 为预防心脏代谢性疾病量身定做的倡议。这项研究将为以下内容提供基础 一项未来试验，以评估实施糖尿病预防计划的有效性，基于临床和 SDoH因素，最终缩小差距。