Abstract
A new era in global health assistance requires a focus on efficiently using limited and declining donor funds. This shift requires better evaluation methods to allocate resources effectively. Most evaluations in low- and middle-income countries (LMICs) examine health disparities within countries, but it is also crucial to assess health outcomes at an inter-country level based on national wealth. Cross-country studies support resource reallocation to the neediest nations and help transition programs like HIV responses within countries with better health infrastructure. This paper presents an unsupervised machine learning method, Principal Component Analysis (PCA), applied to household surveys from 15 African countries to create a universal wealth index that allows multiple countries to be compared on a common scale. Our method places households on a regional wealth scale, enabling cross-country comparisons of health indicators. We used a pooled dataset of 136,086 households from 15- Population-based HIV Impact Assessment (PHIA) countries and validated our universal ranking approach against a local wealth indicator adjusted for macroeconomic differences. The results showed coherence between the macroeconomic-adjusted multinational scale and the PCA-created regional scale, supporting the method's usability for regional household rankings. The proposed method relocates households, as citizens of the world, on a regional wealth scale compared to most surveys that rank them by income placements in their local states. The validation results suggest that the direction and magnitude of mobility of households from national to regional scale in both methods were adequately coherent, ensuring the usability of our approach in ranking households regionally. The PCA-created border-agnostic wealth quintiles enable policymakers to optimize their efficiency improvement efforts, which promises superior efficiency gains over the siloed localized efficiency improvements. Our approach, tested on PHIA-participating countries, can be replicated for similar surveys to study utilization patterns and health outcomes globally.