Frontiers in nutrition
Macro-micro dynamics of child undernutrition in Pakistan: a structural learning-based Bayesian network analysis of household and policy drivers
By Shahid M, Song J, Ali Z and 3 others.
Abstract
Background
Child undernutrition remains a major public health concern and creates health-related inequalities in developing countries, with long-term consequences for human development deficits. Most of these early studies were conducted using conventional econometric techniques; however, understanding the complex, multi-level determinants of undernutrition may require more advanced analytical methods.
Methods
This study, PC algorithm is used for Structural Learning-Based Bayesian Networks [SL-BN] (a graphical model based on directed acyclic graph with joint and conditional associations)-which is conceptually different than the weight-based networks, to examine the macro- and micro-level determinants of child undernutrition in Pakistan. Macro-level analysis combines historical nutrition policy evaluation (critical discourse) with WDI and UNDP data (2000-2022), spanning the MDG and SDG eras. Micro-level analysis draws on the Pakistan Demographic and Health Survey (PDHS 2017-18) to identify socioeconomic drivers of child malnutrition and nutritional inequalities.