A Comparative AI–GIS Spatiotemporal Analysis of Child Health Outcomes in New Zealand and Nigeria (2006–2022): Implications for Equity-Driven Health Policy

Child health is a critical indicator of human development and health system performance. Despite global progress, inequities in child health outcomes remain stark, particularly between high‑income and low‑middle‑income countries. This study applies an integrated Artificial Intelligence Geographic Information Systems (AI–GIS) framework to compare child health trajectories in New Zealand and Nigeria from 2006 to 2022, focusing on spatial inequality, temporal change, and policy effectiveness. Using harmonized national datasets, we examine immunization coverage, sanitation access, and treatment of childhood illnesses in relation to maternal education, household wealth, and place of residence. Results reveal two contrasting inequality regimes: in Nigeria, child health outcomes are shaped by structural inequities linked to governance capacity and infrastructure distribution, while in New Zealand, residual disparities persist within an otherwise mature health system. By distinguishing structural from residual inequality, the study highlights how policy design must adapt to context whether addressing entrenched deprivation in resource‑constrained settings or fine‑tuning equity in advanced systems. Findings underscore the importance of equity‑driven health policy and provide evidence for strengthening child health strategies in both developing and developed contexts.