Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States.
Global Alliance for Improved Nutrition, Washington DC, United States.
J Nutr. 2023 Sep;153(9):2753-2761. doi: 10.1016/j.tjnut.2023.06.024. Epub 2023 Jun 23.
Micronutrient deficiency is a common global health problem, and accurately assessing micronutrient biomarkers is crucial for planning and managing effective intervention programs. However, analyzing micronutrient data and applying appropriate cutoffs to define deficiencies can be challenging, particularly when considering the confounding effects of inflammation on certain micronutrient biomarkers. To address this challenge, we developed the Statistical Apparatus of Micronutrient Biomarker Analysis (SAMBA) R package, a new tool that increases ease and accessibility of population-based micronutrient biomarker analysis. The SAMBA package can analyze various micronutrient biomarkers to assess status of iron, vitamin A, zinc, and B vitamins; adjust for inflammation; account for complex survey design when appropriate; and produce reports of summary statistics and prevalence estimates of micronutrient deficiencies using recommended age-specific and sex-specific cutoffs. In this study, we aimed to provide a step-by-step procedure for how to use the SAMBA R package, including how to customize it for broader use, and made both the package and user manual publicly available on GitHub. SAMBA was validated by comparing results by analyzing 24 data sets on nonpregnant women of reproductive age from 23 countries and 30 data sets on preschool-aged children from 26 countries with those obtained by an independent analyst. SAMBA generated identical means, percentiles, and prevalence of micronutrient deficiencies to those calculated by the independent analyst. In conclusion, SAMBA simplifies and standardizes the process for deriving survey-weighted and inflammation-adjusted (when appropriate) estimates of the prevalence of micronutrient deficiencies, reducing the time from data cleaning to result generation. SAMBA is a valuable tool that facilitates the accurate and rapid analysis of population-based micronutrient biomarker data, which can inform public health research, programs, and policy across contexts.
微量营养素缺乏是一个全球性的健康问题，准确评估微量营养素生物标志物对于规划和管理有效的干预计划至关重要。然而，分析微量营养素数据并应用适当的切点来定义缺乏可能具有挑战性，特别是当考虑到炎症对某些微量营养素生物标志物的混杂影响时。为了解决这一挑战，我们开发了 Statistical Apparatus of Micronutrient Biomarker Analysis（SAMBA）R 包，这是一种新的工具，可提高基于人群的微量营养素生物标志物分析的易用性和可及性。SAMBA 包可以分析各种微量营养素生物标志物，以评估铁、维生素 A、锌和维生素 B 族的状况；调整炎症；在适当情况下考虑复杂的调查设计；并使用推荐的年龄和性别特异性切点生成微量营养素缺乏的汇总统计数据和流行率估计的报告。在这项研究中，我们旨在提供使用 SAMBA R 包的逐步步骤，包括如何对其进行定制以实现更广泛的应用，并在 GitHub 上公开提供该包和用户手册。SAMBA 通过比较分析来自 23 个国家的 24 个非妊娠育龄妇女数据集和来自 26 个国家的 30 个学龄前儿童数据集的结果以及独立分析人员的结果来验证。SAMBA 生成的微量营养素缺乏的平均值、百分位数和流行率与独立分析人员计算的结果相同。总之，SAMBA 简化和标准化了从数据清理到结果生成的衍生具有调查权重和炎症调整（在适当情况下）的微量营养素缺乏流行率估计的过程。SAMBA 是一个有价值的工具，可促进基于人群的微量营养素生物标志物数据的准确和快速分析，从而为跨背景的公共卫生研究、计划和政策提供信息。
