SIMPLE 宏简介，一种提高 24 小时膳食回顾分析和建模可及性的工具。
Introduction to the SIMPLE Macro, a Tool to Increase the Accessibility of 24-Hour Dietary Recall Analysis and Modeling.
机构信息
Institute for Global Nutrition, University of California, Davis, Davis, CA, USA.
Department of Nutrition, University of California, Davis, Davis, CA, USA.
出版信息
J Nutr. 2021 May 11;151(5):1329-1340. doi: 10.1093/jn/nxaa440.
BACKGROUND
Information on long-term dietary intake is often required for research or program planning, but surveys routinely use short-term assessments such as 24-h recalls (24HRs). Methods to reduce the impact of within-person variation in 24HRs, such as the National Cancer Institute (NCI) method, typically require extensive training and skill.
OBJECTIVES
We introduce the Simulating Intake of Micronutrients for Policy Learning and Engagement (SIMPLE) macro, a new tool to increase the accessibility of 24HR analysis. We explain the underlying theory behind the tool and provide examples of potential applications.
METHODS
The SIMPLE macro connects the core NCI statistical code to estimate usual intake distributions and includes additional code to enable advanced analyses such as predictive modeling. The related SIMPLE-Iron macro applies the full probability method to estimate inadequate iron intake, and the SIMPLE-1D macro is used for descriptive or modeling analyses of data with a single 24HR per person. The macros and associated documentations are freely available. We analyzed data from the US National Health and Nutrition Examination Survey (NHANES) and the Cameroon National Micronutrient Survey to compare the SIMPLE macro to 1) the core NCI code using the Estimated Average Requirement cut point method, and 2) the IMAPP software for iron only, and to demonstrate the applications of the SIMPLE macro for estimating usual intake and predictive modeling.
RESULTS
The SIMPLE macro generates identical results to the core NCI code. The SIMPLE-Iron macro also produces estimates of inadequate iron intake comparable to the IMAPP software. The examples demonstrate application of the SIMPLE macro to 1) descriptive analyses of nutrient intake from food and supplements (NHANES), and 2) analyses accounting for breast-milk nutrient intake and modeling fortification and supplementation programs (Cameroon).
CONCLUSIONS
The SIMPLE macros may facilitate the analysis and modeling of dietary data to inform nutrition research, programs, and policy.
背景
长期的饮食摄入信息通常是研究或规划项目所必需的，但调查通常使用 24 小时回顾（24HR）等短期评估方法。为了减少 24HR 中个体内变异的影响，例如美国国家癌症研究所（NCI）方法，通常需要进行广泛的培训和技能。
目的
我们引入了用于政策学习和参与的模拟营养素摄入（SIMPLE）宏，这是一种新的工具，可以提高 24HR 分析的可访问性。我们解释了该工具背后的基本理论，并提供了潜在应用的示例。
方法
SIMPLE 宏将核心 NCI 统计代码连接起来，以估计通常的摄入量分布，并包括额外的代码，以实现预测建模等高级分析。相关的 SIMPLE-Iron 宏应用全概率方法来估计缺铁摄入不足，SIMPLE-1D 宏用于对每人单次 24HR 的数据进行描述性或建模分析。这些宏和相关文档是免费提供的。我们分析了来自美国国家健康和营养检查调查（NHANES）和喀麦隆国家微量营养素调查的数据，将 SIMPLE 宏与 1）使用估计平均需要量切点方法的核心 NCI 代码进行比较，以及 2）仅用于铁的 IMAPP 软件进行比较，并演示了 SIMPLE 宏用于估计通常摄入量和预测建模的应用。
结果
SIMPLE 宏生成与核心 NCI 代码相同的结果。SIMPLE-Iron 宏还产生了与 IMAPP 软件相当的缺铁摄入不足估计值。这些示例展示了 SIMPLE 宏在 1）从食物和补充剂中描述性分析营养素摄入（NHANES），以及 2）考虑母乳中营养素摄入和建模强化和补充计划（喀麦隆）方面的应用。
结论
SIMPLE 宏可能有助于分析和建模饮食数据，以为营养研究、计划和政策提供信息。
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