Using structural equation modelling to assess factors influencing children's growth and nutrition in rural China

Public Health Nutr. 2018 Apr;21(6):1167-1175. doi: 10.1017/S1368980017003494. Epub 2017 Dec 11.

Abstract

Objective: Children's poor growth and nutrition status has serious consequences and therefore it is important to understand its contributing factors.

Design: A community-based interventional study focusing on child feeding was conducted in a rural community in China. Data from the intervention group at baseline (1-4 months of age) and follow-up visits (12 and 18 months of age) were used in the present study (n 236). A structural equation model was generated to explore the effects of family wealth, household food safety, dietary intake, diseases and other factors on the growth and nutrition of young children.

Results: Mother's knowledge and behaviours on household food safety had positive effects on children's weight-for-age Z-score (WAZ; β direct=0·03 and 0·15, respectively, at 12 months of age; β direct=0·02 and 0·08, respectively, at 18 months; P<0·05) and weight-for-length Z-score (WLZ; β direct=0·04 and 0·21, respectively, at 12 months of age; β direct=0·01 and 0·06, respectively, at 18 months; P<0·05). While mothers' feeding behaviours and children's dietary intake at 12 months of age were positively associated with WAZ and/or WLZ at current and later ages, children's diseases were negatively associated with WAZ and WLZ cross-sectionally.

Conclusions: Caregiver's knowledge and feeding behaviours, and children's dietary intake and diseases, are factors influencing the WAZ and WLZ of children. Promoting feeding and health knowledge and behaviours at early stages of childhood can improve children's physical growth at later ages.

Keywords: Growth; Infants and young children; Intervention; Latent variables; Nutrition; Structural equation modelling.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Child Development / physiology*
  • China / epidemiology
  • Feeding Behavior / physiology
  • Female
  • Growth Charts
  • Humans
  • Infant
  • Latent Class Analysis*
  • Male
  • Mothers / statistics & numerical data
  • Nutritional Status / physiology*
  • Rural Population / statistics & numerical data*
  • Socioeconomic Factors
  • Young Adult