The SMART-CV Study: Systems Modeling Approaches to Reducing Disparities in Cardiovascular Diseases

心血管疾病（CVD）是美国的主要死亡原因， 归国尽管不断作出预防努力，但社会和经济弱势群体继续 承受心血管疾病的最大负担。越来越多的证据表明，健康的社会决定因素可以发挥作用 在CVD中产生这种差异的作用。然而，目前旨在减少CVD差异的干预措施 很少关注心血管健康的多个领域和水平的社会决定因素， 影响，但往往侧重于个人层面的因素。此外，决策者在设计干预措施时， 必须考虑在最大限度地提高普通人群健康的干预措施和 最大限度地减少健康差距和未能在这些差距之间取得平衡， 不平等和人口健康状况欠佳。因此，为了有效地减少CVD-冠状动脉的差异， 在预防心脏病和中风方面，迫切需要协助政策决策， 可持续和负担得起的有针对性的保健干预措施，将各领域和各级的社会因素结合起来 该方案的目标是社会和经济弱势群体。K 01提案将 运用几种分析方法，包括关键信息人访谈，综合控制方法， 模拟建模和系统科学（SMSS）方法，以评估干预的影响和成本 有可能有效减少CVD健康差距的战略。为了实现这一目标，博士。 Nianogo将（1）确定在加州实施的干预措施和政策，重点是 心血管健康的社会决定因素在多个领域和影响水平，（2）调查 三种社会政策干预对CVD发病率的影响;（3）建立计算机模拟模型， 预测针对弱势群体的干预措施和政策的长期影响和成本效益 亚群高危亚群这项研究的结果有助于确定有针对性的健康干预措施 有可能减少心血管疾病的健康差异，并为政策决策提供信息。 这个K 01提案建立在候选人以前的研究基础上，该研究开发了计算机模拟 模型（虚拟洛杉矶队列-ViLA）研究肥胖和2型糖尿病的患病率和发病率。 在出生于洛杉矶并从出生到年龄进行跟踪的美国人的代表性样本中的糖尿病 65.将在一个强有力的指导框架内推行这一建议， 在干预研究、心血管疾病、健康差异和模拟建模方面的经验和专业知识， 这是这个项目成功的必要条件。加州大学洛杉矶分校提供了一个适当的环境，进行这种创新 research.在整个提案中，候选人将获得SMSS和CVD差异研究的技能;这些 技能是至关重要的提供博士Nianogo的手段和知识，成为一个成功的独立 研究人员在计算流行病学和SMSS，并确保未来的R 01资金。

ABSTRACT Cardiovascular diseases (CVDs) represent the leading cause of death in the US and are costly to the nation. Despite ongoing prevention efforts, socially and economically disadvantaged populations continue to bear the highest burden of CVDs. There is growing evidence that social determinants of health can play a role in producing such disparities in CVD. Yet current interventions designed to reduce disparities in CVD seldom focus on the social determinants of cardiovascular health across multiple domains and levels of influence but tend to focus on individual-level factors. Furthermore, when designing interventions, policymakers have to consider the trade-offs between interventions that maximize health in the general population and those that minimize health disparities and failure to reach a balance between these can result in increased health disparities and suboptimal population health. Therefore, to efficiently reduce disparities in CVD—coronary heart diseases and stroke, there is a pressing need for assisting policy decision-making in the design of sustainable and affordable targeted health interventions that integrate social factors across domains and levels of influence and which targets the socially and economically disadvantaged populations. This K01 proposal will apply several analytic approaches including key informant interviews, the synthetic control method and simulation modeling and systems science (SMSS) methods to evaluate the impact and costs of intervention strategies that have the potential to efficiently reduce CVD health disparities. To carry out this proposal, Dr. Nianogo will (1) identify interventions and policies that are implemented in California which focused on the social determinants of cardiovascular health across multiple domains and levels of influence, (2) investigate the impact of three societal policy interventions on CVD incidence; (3) develop a computer simulation model to project the long-term impact and cost-effectiveness of interventions and policies targeting disadvantaged subpopulations, high-risk subpopulations. Findings from this study help identify targeted health interventions that have the potential to reduce health disparities in CVD and inform policy decision-making. This K01 proposal builds on the candidate’s previous research that developed a computer simulation model (Virtual Los Angeles Cohort—ViLA) to study the prevalence and incidence of obesity and type 2 diabetes among a representative sample of U.S. persons born in Los Angeles and followed from birth to age 65. The proposal will be pursued within the context of a strong mentorship that has adequate extensive experience and expertise on intervention research, CVDs, health disparities and simulation modeling that is necessary for the success of this project. UCLA provides an adequate environment to conduct such innovative research. Throughout this proposal, the candidate will gain skills in SMSS and CVD disparities research; these skills are critical to providing Dr. Nianogo with the means and knowledge to become a successful independent investigator in computational epidemiology and SMSS and to secure future R01 funding.