Alternative approaches for creating a wealth index: the case of Mozambique

Abstract

Introduction: The wealth index is widely used as a proxy for a household's socioeconomic position (SEP) and living standard. This work constructs a wealth index for the Mopeia district in Mozambique using data collected in year 2021 under the BOHEMIA (Broad One Health Endectocide-based Malaria Intervention in Africa) project.

Methods: We evaluate the performance of three alternative approaches against the Demographic and Health Survey (DHS) method based wealth index: feature selection principal components analysis (PCA), sparse PCA and robust PCA. The internal coherence between four wealth indices is investigated through statistical testing. Validation and an evaluation of the stability of the wealth index are performed with additional household income data from the BOHEMIA Health Economics Survey and the 2018 Malaria Indicator Survey data in Mozambique.

Results: The Spearman's rank correlation between wealth index ventiles from four methods is over 0.98, indicating a high consistency in results across methods. Wealth rankings and households' income show a strong concordance with the area under the curve value of ~0.7 in the receiver operating characteristic analysis. The agreement between the alternative wealth indices and the DHS wealth index demonstrates the stability in rankings from the alternative methods.

Conclusions: This study creates a wealth index for Mopeia, Mozambique, and shows that DHS method based wealth index is an appropriate proxy for the SEP in low-income regions. However, this research recommends feature selection PCA over the DHS method since it uses fewer asset indicators and constructs a high-quality wealth index.

Keywords: health services research; indices of health and disease and standardisation of rates; public health.

Figure 1

Coefficients of asset indicators from classical PCA, robust PCA, feature selection PCA and sparse PCA in Mopeia, Mozambique. PCA, principal components analysis.

Figure 2

Numbers of households under different wealth quintiles created using classical PCA, feature selection PCA, robust PCA and sparse PCA. PCA, principal components analysis.

Figure 3

ROC curves and AUC values for classification results of feature selection PCA wealth index according to the income-based poverty status (N=138). AUC, area under the curve; PCA, principal components analysis.

Figure 4

Correlation between DHS household wealth index and the feature selection PCA wealth index, and numbers of households under different wealth quintiles created using DHS wealth rank and feature selection PCA wealth rank. DHS, Demographic and Health Survey; PCA, principal components analysis.