A Statistical and Visual Analytics Approach to Identify Risk Factors for Severe Physical Punishment Against Children Across 54 Countries Using MICS Data

Violent discipline against children is a recognized global public health problem associated with adverse physical, cognitive, and mental health outcomes. This study proposes a visual analytics approach to support public health decision-making by analyzing determinants of severe physical punishment among children in 54 low- and middle-income countries using MICS6 data. We integrated child- and caregiver-level microdata and applied statistical association tests combined with structured visual exploration techniques to identify high-risk population groups. Maternal education was associated with lower exposure to severe physical punishment. Similar associations were observed for parental age and child age, while boys showed slightly higher exposure. The proposed visual pipeline enhances the interpretability of multivariate social determinants, facilitating the identification of vulnerable groups and supporting preventive health policies. These findings highlight the potential of data visualization as a computational tool for public health surveillance and policy design.