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Deep data science to prevent and treat growth faltering in Maya children

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journal contribution
posted on 01.04.2016, 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.

Funding

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.

History

School

  • Sport, Exercise and Health Sciences

Published in

European Journal of Clinical Nutrition

Citation

VARELA 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

Version

VoR (Version of Record)

Publisher statement

This 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/

Acceptance date

21/03/2016

Publication date

2016

Notes

This 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/

ISSN

1476-5640

Language

en