Accelerated Failure Time Model with Spatial Dependency and Covariates Interaction Structures: Application to Under-Five Mortality in Nigeria (Journal of Pure and Applied Sciences (Science Forum), 2025)
Under-five mortality (U5M) remains a pressing public health concern in Nigeria, characterized by persistent regional disparities and influenced by a complex interplay of socioeconomic, demographic, and healthcare-related factors. This study extends the Bayesian Weibull Accelerated Failure Time (AFT) modeling framework by incorporating spatial dependency and covariate interaction structures to better elucidate the determinants of U5M across Nigeria. Utilizing nationally representative data from the 2018 Nigeria Demographic and Health Survey (NDHS), the study evaluates multiple model specifications, including the AFT model with standard covariate interaction structures (SCIS) and logistictype covariate interaction structures (LTCIS). Spatial clustering at the state level is accounted for using Intrinsic Conditional Autoregressive (ICAR) priors. Model comparison based on Deviance Information Criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC) indicates that the Weibull AFT model with spatial effects and SCIS structure provides the best fit (DIC = 152,058.77; WAIC = 152,049.57), outperforming models without spatial or interaction terms. Key findings highlight breastfeeding as the most protective factor against U5M (time ratio [TR] = 52.405), with its effect further enhanced among children from the richest households (TR up to 3.438). Other significant protective factors include longer birth intervals (24–33 months: TR = 1.346; >33 months: TR = 2.435), maternal education, and higher household wealth. The spatial analysis uncovers distinct geographic disparities, with elevated U5M risks concentrated in several southern states, underscoring the role of regional context. This study advances the methodological and substantive understanding of child mortality by quantifying both spatial heterogeneity and synergistic interactions among key predictors. The findings provide actionable insights for policymakers aiming to design geographically and socioeconomically targeted interventions. Moreover, the research underscores the value of integrating interaction structures within spatial survival models to enhance the precision and relevance of public health analytics.