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Deep data science to prevent and treat growth faltering in Maya children
journal contributionposted on 2016-04-01, 10:19 authored by Maria Ines Varela Silva, Barry Bogin, J. Andres Galvez-Sobral, Federico Dickinson, Susana Monserrat-RevilloSusana Monserrat-Revillo, Growth and 6 Development – Knowledge Integration (HBGDki) Initiative Healthy Birth
The Maya people are descended from the indigenous inhabitants of southern Mexico, Guatemala, and adjacent regions of Central America. In Guatemala, 50% of infants and children are stunted (very low height-for-age), and some rural Maya regions have >70% children stunted. A large, longitudinal, intergenerational, database was created to (1) provide deep data to prevent and treat somatic growth faltering and impaired neurocognitive development; (2) detect key dependencies and predictive relations between highly complex, time-varying, and interacting biological and cultural variables; and (3) identify targeted multifactorial intervention strategies for field testing and validation. Contributions to this database included data from the Universidad del Valle de Guatemala Longitudinal Study of Child and Adolescent Development, child growth and intergenerational studies among the Maya in Mexico, and studies about Maya migrants in the United States.
The authors are grateful for support from the Bill & Melinda Gates Foundation, the Universidad del Valle de Guatemala, and Centro de Investigación y de Estudios Avanzados (CINVESTAV), Unidad Mérida, Mexico.
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Published inEuropean Journal of Clinical Nutrition
CitationVARELA SILVA, M.I. ...et al., 2016. Deep data science to prevent and treat growth faltering in Maya children. European Journal of Clinical Nutrition, 70 (6), pp. 679-680.
Publisher© Macmillan Publishers Limited
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Publisher statementThis work is made available according to the conditions of the Creative Commons Attribution (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/ by/4.0/
NotesThis is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License, http://creativecommons.org/licenses/ by/4.0/