The influence of pricing and nutrient profiling on food purchases

描述(申请人提供)：操纵食品价格是调整食品购买的最有力的方法之一。另一种方法是使用营养简档通过购买点获取营养信息。这笔赠款的目的是评估1)25%的税收和补贴对最不营养和最营养的食品的独立和综合影响，基于其营养状况，以及2)购买点营养状况对所购买食品的营养密度、能量、能量密度和特定营养素的影响。这项研究将在不同种族和不同收入的成年人中进行，他们为家人购买食物。将使用2×2析因设计将800名购物者随机分为1)具有通常营养信息的控制组，2)仅关于杂货店所有食品的营养概况信息，3) 对营养价值最低的20%的食品征税，只对营养价值最高的20%的食品进行补贴，以及4)营养成分分析加上差额税收和补贴。营养分析将基于NuVal，这是一个综合的营养分析系统，基于考虑到积极因素(纤维、维生素、矿物质、欧米茄3脂肪酸等)的算法，对食物进行1-100分的评分。以及食物的负面因素(反式脂肪、盐、糖等)，以及蛋白质和脂肪质量、能量密度和血糖负荷等因素。在一项对10万人进行的队列研究中，NuVal得分预测了包括全因死亡率在内的健康结果，得分越高(营养价值越好)，各种疾病的风险就越低。具体目标1a将评估营养概况和价格变化对购买的所有食品的平均营养密度/100千卡的主要和交互影响，以及特定目标1a将评估营养概况和/或价格变化对购买的食品的总能量和能量密度的主要和交互影响，包括纤维、添加糖、饱和脂肪和钠等特定营养素的平均NuVal评分和平均值，以及对购买食品的平均NuVal评分和常量营养素组成的主要和交互影响。具体目标2将评估营养概况和/或价格变化对提高营养密度/100千卡的成本的主要和相互作用的影响。具体目标3将评估收入、体重指数或饮食限制是否缓和了营养概况和价格变化对营养密度/100千卡的影响。

DESCRIPTION (provided by applicant): Manipulating food prices is one of the most powerful ways to modify food purchasing. An alternative approach is by point of purchase nutrition information using nutrient profiling. The goal of this grant is to assess the independent and combined effects of 1) 25% taxes and subsidies on the least and most nutritious foods based on their nutrient profiles and 2) point of purchase nutrient profiling on nutrient density, energy, energy density and specific nutrients of foods purchased. The study will be undertaken in a sample of ethnically and income diverse adults who do the food shopping for their families. A 2 x 2 factorial design will be used to randomize 800 shoppers to 1) a control arm with usual nutrition information, 2) nutrient profiling information only on all foods in the grocery store, 3) taxes on the 20% lowest nutritional value foods plus subsidies on the 20% highest nutritional value foods only, and 4) nutrient profiling plus the differential taxes and subsidies. Nutrient profiling will be based on NuVal, a comprehensive nutrient profiling system that scores foods on a 1-100 scale based on an algorithm that takes in to account positive (fiber, vitamins, minerals, Omega 3 fatty acids, etc.) and negative aspects of food (trans fat, salt, sugar, etc,) as well as factors such as protein and fat quality, energy density, and glycemic load. NuVal scores predict health outcomes, including all-cause mortality, in a cohort study of 100,000 people, with higher scores (better nutritional value) associated with lower risk for a variety of diseases. Specific Ai 1 will assess the main and interactive effects of point of purchase nutrient profiling and price changes on average nutrient density/100 kcal of all foods purchased, and Specific Aim 1a will assess the main and interactive effects of nutrient profiling and/or price changes on total energy and energy density of foods purchased, average NuVal scores and average values of specific nutrients including fiber, added sugar, saturated fat and sodium, and the main and interactive effects on average NuVal scores and macronutrient composition of purchased foods. Specific Aim 2 will assess the main and interactive effects of nutrient profiling and/or price changes on cost to improve nutrient density/100 kcal. Specific Aim 3 will assess the whether income, body mass index or dietary restraint moderate the effect of nutrient profiling and price changes on nutrient density/100 kcal.