Streamlining micronutrient biomarker statistical analysis in populations: an introduction to the SAMBA R package

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cg.contributor.crpAgriculture for Nutrition and Health
cg.contributor.donorCGIAR Trust Funden
cg.identifier.doihttps://doi.org/10.1016/j.tjnut.2023.06.024en
cg.identifier.projectIFPRI - HarvestPlusen
cg.isijournalISI Journalen
cg.issn0022-3166en
cg.issue9en
cg.journalJournal of Nutritionen
cg.reviewStatusPeer Reviewen
cg.volume153en
dc.contributor.authorLuo, Hanqien
dc.contributor.authorBeal, Tyen
dc.contributor.authorBlake, Tinekaen
dc.contributor.authorZeiler, Madeleineen
dc.contributor.authorGeng, Jiaxien
dc.contributor.authorWerner, E. Rochelleen
dc.contributor.authorAddo, O. Yawen
dc.contributor.authorSuchdev, Parminderen
dc.contributor.authorYoung, Melissa F.en
dc.date.accessioned2025-01-29T12:58:17Zen
dc.date.available2025-01-29T12:58:17Zen
dc.identifier.urihttps://hdl.handle.net/10568/171515
dc.titleStreamlining micronutrient biomarker statistical analysis in populations: an introduction to the SAMBA R packageen
dcterms.abstractMicronutrient 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.en
dcterms.accessRightsOpen Access
dcterms.bibliographicCitationLuo, 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.2023.06.024en
dcterms.extentpp. 2753-2761en
dcterms.issued2023-09en
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.publisherElsevieren
dcterms.subjectmicronutrient deficienciesen
dcterms.subjecthealthen
dcterms.subjectinflammationen
dcterms.subjectdataen
dcterms.subjectwomenen
dcterms.subjectchildrenen
dcterms.subjectpublic healthen
dcterms.subjectstatistical methodsen
dcterms.typeJournal Article

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