Studying social factors in sodium consumption to reduce hypertension disparities

描述（由申请人提供）：本申请是为Sanjay Basu博士申请的，他是一名内科流行病学家，致力于心血管疾病差异的统计和建模研究。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方面的职业重点；(2)学习如何实施模拟心血管疾病干预影响的“系统科学”数学建模方法；(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未解决的主要问题。为了实现这些目标，巴苏博士组建了一个具有营养及其与心血管疾病风险关系专业知识的指导团队；心血管疾病流行病学差异；还有数学建模。Basu博士的研究计划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）已经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巴苏博士的研究将测试社会因素与低社会地位人群中钠摄入量之间关系的假设，这些人群经历了高血压不受控制的最高患病率在目标1中，应用于国家健康和营养检查调查的回归树分析将检验这样一个假设，即可以使用几个关键的社会人口变量来预测人群中钠摄入量的变化该分析将确定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，我们将检验这样一个假设，即一个经济计划激励低社会地位人群增加每月去杂货店购物的次数，可以导致高钠消费者用新鲜农产品代替包装产品，从而降低他们的钠摄入量。为了更好地评估因果关系，我们将分析特拉华州、新泽西州和宾夕法尼亚州的一个独特的“准自然实验”，在这个实验中，一组领取食品券的家庭被随机分配每月领取两次福利，而不是每月一次。尽管与匹配的对照组相比，这些家庭的每月总体杂货预算相同，但这一实验导致他们更频繁地购买杂货。Aims 1和2的结果将被纳入Aim 3的高血压模拟模型。我们将构建一个基于主体的模型，将目标2中的计划与另外两个计划（包装正面营养标签和蔬菜代金券计划）的影响进行比较，这两个计划试图影响消费者减少钠的消耗。该模型将估计减少国家高血压差异的干预措施的影响和成本效益。这将构成R01应用的基础，以研究针对消费者食品选择行为的新型减钠策略。该提案解决了NHLBI战略计划挑战3.1，该挑战鼓励社会科学研究结合“系统科学”建模方法来调查社会因素和政策干预措施，以减少健康差距

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