Streamlining Micronutrient Biomarker Statistical Analysis in Populations : An Introduction to the SAMBA R Package
Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:153 |
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| Enthalten in: |
The Journal of nutrition - 153(2023), 9 vom: 23. Sept., Seite 2753-2761 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Luo, Hanqi [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 11.09.2023 Date Revised 25.09.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.tjnut.2023.06.024 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM35856347X |
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| 520 | |a Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, U.S. Gov't, P.H.S. | |
| 650 | 4 | |a Research Support, U.S. Gov't, Non-P.H.S. | |
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| 650 | 7 | |a Trace Elements |2 NLM | |
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| 700 | 1 | |a Blake, Tineka |e verfasserin |4 aut | |
| 700 | 1 | |a Zeiler, Madeleine |e verfasserin |4 aut | |
| 700 | 1 | |a Geng, Jiaxi |e verfasserin |4 aut | |
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| 700 | 1 | |a Addo, O Yaw |e verfasserin |4 aut | |
| 700 | 1 | |a Suchdev, Parminder S |e verfasserin |4 aut | |
| 700 | 1 | |a Young, Melissa F |e verfasserin |4 aut | |
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