Complex Associations between Environmental Factors and Child Growth: Novel Mixed-Methods Approach

Abstract

Environmental risks associated with child growth are complex, and intervention effectiveness has been consistently poor. To improve effectiveness, proper intervention points inside the complex system must be identified. Integrating site-specific knowledge, machine learning, and statistical modeling offers a powerful approach to addressing this problem. In this study, a novel four-step method is employed to identify the key environmental factors to low child height-for-age in Guatemala. The four steps included (1) the development of a region-specific, ranked list of contributing factors to low child height-for-age via informal interviews and literature; (2) the application of a clustering method to a large regional data set; (3) the identification of the top six ranked variables shared between Step 1 and 2; and (4) the analysis of the clustered, regional data set in a multigroup path analysis incorporating the top six ranked variables, diarrheal prevalence, and child height-for-age. Results suggested that an increase in diarrheal prevalence was not consistently associated with a decrease in child height-for-age. Having soap for handwashing was significantly correlated with lower diarrhea and higher height-for-age. The effect was larger in the poorer population. Finally, disease in maize was significantly correlated with lower diarrhea. This method provided an approach to reducing, modeling, and ranking large numbers of environmental risk factors to child growth, identifying potential regional intervention points.

Department(s)

Mathematics and Statistics

Second Department

Electrical and Computer Engineering

Third Department

Civil, Architectural and Environmental Engineering

Research Center/Lab(s)

Center for High Performance Computing Research

Keywords and Phrases

Cluster analysis; Regression analysis; Child height-for-age; Clustering methods; Diarrheal prevalence; Guatemala; Path analysis; Learning systems; Zea mays

International Standard Serial Number (ISSN)

0733-9372; 1943-7870

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2019 American Society of Civil Engineers (ASCE), All rights reserved.

Publication Date

01 Jun 2019

Link to Full Text

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