Streamlining micronutrient biomarker statistical analysis in populations: an introduction to the SAMBA R package
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, partic...
| Autores principales: | , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2023
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/171515 |
| _version_ | 1855515516016263168 |
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| author | Luo, Hanqi Beal, Ty Blake, Tineka Zeiler, Madeleine Geng, Jiaxi Werner, E. Rochelle Addo, O. Yaw Suchdev, Parminder Young, Melissa F. |
| author_browse | Addo, O. Yaw Beal, Ty Blake, Tineka Geng, Jiaxi Luo, Hanqi Suchdev, Parminder Werner, E. Rochelle Young, Melissa F. Zeiler, Madeleine |
| author_facet | Luo, Hanqi Beal, Ty Blake, Tineka Zeiler, Madeleine Geng, Jiaxi Werner, E. Rochelle Addo, O. Yaw Suchdev, Parminder Young, Melissa F. |
| author_sort | Luo, Hanqi |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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- and sex-specific cutoffs. We have provided 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 from analyzing 24 datasets on non-pregnant women of reproductive age from 23 countries and 30 datasets on preschool-age 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. |
| format | Journal Article |
| id | CGSpace171515 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1715152025-02-04T18:05:54Z Streamlining micronutrient biomarker statistical analysis in populations: an introduction to the SAMBA R package Luo, Hanqi Beal, Ty Blake, Tineka Zeiler, Madeleine Geng, Jiaxi Werner, E. Rochelle Addo, O. Yaw Suchdev, Parminder Young, Melissa F. micronutrient deficiencies health inflammation data women children public health statistical methods 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- and sex-specific cutoffs. We have provided 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 from analyzing 24 datasets on non-pregnant women of reproductive age from 23 countries and 30 datasets on preschool-age 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. 2023-09 2025-01-29T12:58:17Z 2025-01-29T12:58:17Z Journal Article https://hdl.handle.net/10568/171515 en Open Access Elsevier Luo, Hanqi; Beal, Ty; Blake, Tineka; Zeiler, Madeleine; Geng, Jiaxi; Werner, E. Rochelle; Addo, O. Yaw; Suchdev, Parminder; and Young, Melissa. 2023. Streamlining micronutrient biomarker statistical analysis in populations: an introduction to the SAMBA R package. Journal of Nutrition 153(9): 2753-2761. https://doi.org/10.1016/j.tjnut.2023.06.024 |
| spellingShingle | micronutrient deficiencies health inflammation data women children public health statistical methods Luo, Hanqi Beal, Ty Blake, Tineka Zeiler, Madeleine Geng, Jiaxi Werner, E. Rochelle Addo, O. Yaw Suchdev, Parminder Young, Melissa F. Streamlining micronutrient biomarker statistical analysis in populations: an introduction to the SAMBA R package |
| title | Streamlining micronutrient biomarker statistical analysis in populations: an introduction to the SAMBA R package |
| title_full | Streamlining micronutrient biomarker statistical analysis in populations: an introduction to the SAMBA R package |
| title_fullStr | Streamlining micronutrient biomarker statistical analysis in populations: an introduction to the SAMBA R package |
| title_full_unstemmed | Streamlining micronutrient biomarker statistical analysis in populations: an introduction to the SAMBA R package |
| title_short | Streamlining micronutrient biomarker statistical analysis in populations: an introduction to the SAMBA R package |
| title_sort | streamlining micronutrient biomarker statistical analysis in populations an introduction to the samba r package |
| topic | micronutrient deficiencies health inflammation data women children public health statistical methods |
| url | https://hdl.handle.net/10568/171515 |
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