Unraveling the Multifactorial Roots of Stunting: The Critical Roles of Complementary Feeding and Sanitation in Mandailing Natal, Indonesia

Abstract

Stunting remains a significant public health issue globally, affecting 140 million children under five years. In Indonesia, stunting prevalence has decreased over time but remains high in certain regions, with Mandailing Natal Regency showing rates of 45.76% in 2018 and 49.91% in 2019. Based on the Indonesian Nutritional Status Survey (SSGI), there has been a decrease to 47.7 percent in 2022, 34.2 percent in 2022 and 20.7 percent in 2023. This study aimed to identify the key factors contributing to stunting in Mandailing Natal Regency, North Sumatera, and develop a community-based prevention model. A cross-sectional study was conducted in Mandailing Natal Regency. The study population comprised children under two years, and participants were selected using proportional cluster random sampling. Data were collected from 399 mothers, health workers, and village midwives using structured interviews. Information on socio-economic status, maternal nutrition knowledge, environmental sanitation, and infant feeding practices was gathered. The collected data were analyzed using Structural Equation Modeling (SEM) to determine the factors influencing stunting and to evaluate the relationships between socio-economic status, feeding practices, and sanitation. The SEM analysis revealed that inappropriate complementary feeding practices were the most significant factor influencing stunting (coefficient = 0.778, p = 0.001), followed by poor sanitation (coefficient = 0.077, p = 0.031). Socio-economic status did not directly influence stunting but had an indirect impact through feeding and sanitation practices. Conclusions: Inadequate complementary feeding and poor sanitation are key contributors to stunting in Mandailing Natal Regency. Targeted community interventions addressing these factors are crucial for reducing stunting in this high-prevalence region. Keywords Interventions, Nutrition, Sanitation, Socioeconomics, Structural Equation Modeling (SEM)