Studying social factors in sodium consumption to reduce hypertension disparities

描述（由申请人提供）：本申请适用于Sanjay Basu博士，他是一位在心血管疾病差异的统计和建模研究领域建立自己的流行病学家。该奖项将使Basu博士能够：（1）发展营养流行病学和心血管疾病差异的职业重点;（2）学习如何实施“系统科学”数学建模方法，模拟心血管疾病干预措施的影响;（3）获得必要的内容专业知识，以解决心血管疾病差异流行病学中的主要未决问题。为了实现这些目标，Basu博士组建了一个具有营养及其与心血管疾病风险关系的专业知识的指导团队;心血管疾病差异的流行病学;和数学建模。Basu博士的研究建议解决了不同社会经济地位（SES）群体之间高血压患病率的差异。医学研究所（IOM）已宣布降低钠摄入量是减少高血压相关心血管疾病的国家目标。2 Basu博士的研究将测试关于社会因素与低SES人群中钠摄入量之间关系的假设，这些人群经历了最高的不受控制的高血压患病率。3在目标1中，应用于国家健康和营养检查调查的回归树分析4将检验以下假设，即可以使用几个关键的社会人口变量预测人口中钠消费量的变化。哪些社会因素可以最好地解释美国人群钠摄入量的异质性。在目标2中，我们将检验一个假设，即一个激励低社会经济地位人群增加每月去杂货店购物次数的经济计划可以引导高钠消费者用新鲜农产品替代包装产品，从而降低他们的钠消费量。6 -8为了更好地评估因果关系，我们将分析特拉华州的一个独特的“准自然实验”，在新泽西和宾夕法尼亚州，一部分领取食品券的家庭被随机分配到每月两次而不是每月一次领取救济金，这导致了更频繁的杂货购物，尽管与匹配的对照组相比，他们每月的杂货总预算相同。目标1和2的结果将纳入目标3的高血压模拟模型。将构建一个基于代理人的模型，以比较目标2中的计划与其他两个计划的影响-包装前营养标签9和蔬菜券计划10-试图影响消费者减少钠消费。该模型将估计干预措施对减少全国高血压差异的影响和成本效益。这将成为R 01应用的基础，以研究针对消费者食品选择行为的新型钠减少策略。该提案涉及NHLBI战略计划挑战3.1，鼓励社会科学研究纳入“系统科学”建模方法，以调查社会因素和政策干预措施，以减少健康风险。

DESCRIPTION (provided by applicant): This application is for Dr. Sanjay Basu, a physician-epidemiologist establishing himself in the field of statistical and modeling research on cardiovascular disease disparities. This award will allow Dr. Basu to: (1) develop a career focus in nutritional epidemiology and cardiovascular disease disparities; (2) learn how to implement "systems science" mathematical modeling methods that simulate the impact of cardiovascular disease interventions; and (3) gain the necessary content expertise to address major unanswered questions in the epidemiology of cardiovascular disease disparities. To achieve these goals, Dr. Basu has assembled a mentoring team with expertise on nutrition and its relationship to cardiovascular disease risk; the epidemiology of cardiovascular disease disparities; and mathematical modeling. Dr. Basu's research proposal addresses disparities in the prevalence of hypertension between groups of different socioeconomic status (SES). The Institute of Medicine (IOM) has declared lower sodium consumption to be a national goal to reduce hypertension-related cardiovascular disease.2 Dr. Basu's research will test hypotheses about the relationship between social factors and sodium consumption among low-SES populations experiencing the highest prevalence of uncontrolled hypertension.3 In Aim 1, a regression tree analysis4 applied to the National Health and Nutrition Examination Survey will test the hypothesis that variations in sodium consumption within the population can be predicted using a few key sociodemographic variables.5 The analysis will identify what social factors can best explain heterogeneities in sodium intake within the US population. In Aim 2, we will then test the hypothesis that an economic program incentivizing low-SES populations to increase the number of times per month they go grocery shopping can lead high-sodium consumers to substitute packaged products with fresh produce, thereby lowering their sodium consumption.6-8 To better assess causality, we will analyze a unique "quasi-natural experiment" in Delaware, New Jersey and Pennsylvania in which a subset of households on food stamps were randomly allocated to receive benefits twice monthly instead of once monthly, which induced more frequent grocery shopping despite having the same overall monthly grocery budget as matched controls. The results of Aims 1 and 2 will be incorporated into a simulation model of hypertension in Aim 3. An agent-based model will be constructed to compare the impact of the program in Aim 2 to two other programs- front-of-package nutrition labeling9 and a vegetable voucher plan10-that attempt to influence consumers to reduce sodium consumption. The model will estimate the impact and cost-effectiveness of the interventions for reducing national hypertension disparities. This will form the basis of an R01 application to study novel sodium reduction strategies that target consumer food choice behaviors. This proposal addresses NHLBI strategic plan challenge 3.1, which encourages social science research incorporating "systems science" modeling methods to investigate social factors and policy interventions to reduce health disparities.11