IDENTIFYING GLOBAL UNDER-FIVE MORTALITY HOTSPOTS BASED ON SPATIAL OUTLIER DETECTION AND ROBUST REGRESSION ESTIMATORS: POLICY INSIGHTS - Bappah Mohammed, Mohammed; Abdullahi Baba, Ishaq; Mohammed Madaki, Rabiu (FUDMA Journal of Sciences, 2026)
Under-five mortality remains a critical public health problem driven by multiple socioeconomic, health, and environmental factors. The severity of this problem varies across regions, depending on the underlying contextual characteristics. Understanding the spatial distribution and temporal pattern of under-five mortality can provide actionable insights for targeted interventions in vulnerable areas and help identify key sources of variation in mortality. In this study, we apply multivariate outlier detection and classification methods to identify countries at high risk of under-five mortality, using the robust multivariate CovMCD and cellMCD techniques. We exploit the concepts of regular, cellwise, and rowwise outliers to classify the risk levels of different countries. The analysis uses 2023 global under-five mortality data for 199 countries obtained from the United Nations Inter-agency Group for Child Mortality Estimation and evaluates robust regression methods for modelling global under-five mortality (U5MR). Our results show that spatial maps derived from CovMCD and cellMCD identify Africa as the most vulnerable region in terms of under-five mortality risk. Furthermore, we apply four regression estimators: ordinary least squares (OLS), least absolute deviations (LAD), least trimmed squares (LTS), and MM to compare their performance in the presence of cellwise and rowwise contamination. Both the real-data analysis and simulation study indicate that the MM estimator outperforms its competitors in terms of average coefficient RMSE and RMSPE across different outlier scenarios. Overall, our findings demonstrate that conventional OLS models can be misleading in the presence of multivariate outliers and reinforce the value of robust methods for policy-relevant child mortality modelling.
Keywords: Outlier Detection; Robust Regression; Mortality Rate; Estimators