A robust composite indicator framework for evaluating Sustainable Development Goal 3 using benefit‐of‐the‐doubt models and principal component analysis

Abstract This study proposes a novel benefit‐of‐the‐doubt (BOD) model for constructing composite indicators (CIs) to assess Sustainable Development Goal 3 across the World Health Organization (WHO) member states. To address the BOD model's limited ability to differentiate among states with many sub‐indicators, we introduce a common weight BOD (CWBOD) model to improve cross‐country comparability. To improve the model's discriminatory power, we apply principal component analysis (PCA) to reduce the number of sub‐indicators, using the resulting principal components as inputs to the BOD model. To account for the uncertainty due to possible information loss in PCA, we further develop a robust BOD (RBOD) model. The final CI scores are computed using the geometric mean of the BOD, CWBOD, and RBOD scores. We apply this integrated framework to compute a Public Health Index for 177 WHO member states, enabling a more precise and robust evaluation of global public health performance.