Evidence brief: Formulation and validation of a regional household wealth index for sub-Saharan Africa

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.

Introduction

As the transition of HIV care to country ownership progresses, it is important to ensure data-driven policies guide this shift to preserve past achievements [1,2]. Since its founding in 2003, the PEPFAR program has saved over 25 million lives and granted over 20.5 million people access to antiretroviral therapy [3]. To sustain these gains as resources become constrained, several intra-country analyses have been conducted to guide the reallocation of funds and resources within comparative groups with poorer access to HIV treatment or outcomes. Researchers have used this approach to address access and outcome gaps in HIV treatment based on urban/rural residence, wealth, sex, and age [4]. Although such intra-country studies are useful, an approach is needed that facilitates reallocation analyses across multiple countries in a consistent fashion.

Studies indicate that wealthier individuals tend to have better outcomes within countries [5–7]. Asset-based wealth indices are commonly used to assess households’ socio-economic status in low- and middle-income countries (LMICs) when reliable income data is unavailable. Different approaches exist to measure wealth and poverty, including relative measures such as the DHS wealth quintiles [8,9] and absolute measures such as the World Bank’s international poverty thresholds [10,11]. Relative measures are useful for assessing inequality within countries [12], while absolute measures enable cross-country comparisons but may overlook within-country disparities [13]. Our study builds on the relative approach by creating a regional index that allows households across sub-Saharan Africa to be ranked on the same scale. Surveys like the Demographic Health Survey (DHS) provide accessible data, making wealth measurements in LMICs reliable. These methods effectively explain variations in household health status and outcomes [14–16]. The link between wealth and HIV services has prompted discussions on reallocating resources to underserved populations [4,17–19]. Much of this research has been done using country-level household wealth indices [20,21].