Relative importance of prenatal and postnatal determinants of stunting: data mining approaches to the MINIMat cohort, BangladeshShow others and affiliations
2019 (English)In: BMJ Open, E-ISSN 2044-6055, Vol. 9, no 8
Article in journal (Refereed) Published
Abstract [en]
Introduction WHO has set a goal to reduce the prevalence of stunted child growth by 40% by the year 2025. To reach this goal, it is imperative to establish the relative importance of risk factors for stunting to deliver appropriate interventions. Currently, most interventions take place in late infancy and early childhood. This study aimed to identify the most critical prenatal and postnatal determinants of linear growth 0–24 months and the risk factors for stunting at 2 years, and to identify subgroups with different growth trajectories and levels of stunting at 2 years.
Methods Conditional inference tree-based methods were applied to the extensive Maternal and Infant Nutrition Interventions in Matlab trial database with 309 variables of 2723 children, their parents and living conditions, including socioeconomic, nutritional and other biological characteristics of the parents; maternal exposure to violence; household food security; breast and complementary feeding; and measurements of morbidity of the mothers during pregnancy and repeatedly of their children up to 24 months of age. Child anthropometry was measured monthly from birth to 12 months, thereafter quarterly to 24 months.
Results Birth length and weight were the most critical factors for linear growth 0–24 months and stunting at 2 years, followed by maternal anthropometry and parental education. Conditions after birth, such as feeding practices and morbidity, were less strongly associated with linear growth trajectories and stunting at 2 years.
Conclusion The results of this study emphasise the benefit of interventions before conception and during pregnancy to reach a substantial reduction in stunting.
Place, publisher, year, edition, pages
BMJ Publishing Group Ltd, 2019. Vol. 9, no 8
National Category
Public Health, Global Health and Social Medicine Computer Sciences Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-159347DOI: 10.1136/bmjopen-2018-025154ISI: 000502537200025PubMedID: 31383692Scopus ID: 2-s2.0-85070315727OAI: oai:DiVA.org:liu-159347DiVA, id: diva2:1341376
Note
Funding agencies: icddr,b; United Nations Childrens Emergency Fund; Swedish International Development Cooperation Agency; UK Medical Research CouncilMedical Research Council UK (MRC); Swedish Research CouncilSwedish Research Council; Department for International Developmen
2019-08-082019-08-082025-02-20Bibliographically approved