A novel cluster-based algorithm to estimate socio-economic status for health equity surveillance in sub-Saharan Africa
Georges Nguefack-Tsague, PhD, Associate Professor of Statistics at the University of Yaoundé I in Cameroon, with affiliate faculty positions at AIMS-Cameroon and Buea
Alan Hubbard, PhD, Professor of Biostatistics at the University of California, Berkely
Catherine Juillard MD, MPH, trauma surgeon, Associate Professor of Surgery at University of California, Los Angeles and co-director of UCLA Department of Surgery’s Program for the Advancement of Surgical Equity (PASE)
Salome Maswime, MD, PhD, obstetrician/gynecologist and Associate Professor and the Head of the Global Surgery Division at the University of Cape Town
Fanny Dissak-Delon, MD, PhD, MPH, Public Health Officer, Cameroon Ministry of Public Health Littoral Regional Delegation
Our long-term goal is to improve health equity.
As health disparities widen globally, achieving improvements in health equity between and within regions is an urgent priority. Currently, in sub-Saharan Africa (SSA), health equity surveillance is not regularly implemented, in large part due to the difficulty in measuring socioeconomic status (SES). The lack of a quick, reliable metric of SES severely hinders researchers’ ability to study and reduce health disparities.
Our overall objective is to apply a novel cluster-based algorithm to estimate socio-economic status for health equity surveillance in sub-Saharan Africa.
We have developed a k-medoids clustering-based algorithm that generates simple, population-specific models of economic status using only four or five of the 29 to 36 Demographic and Health Survey (DHS) assets variables in Cameroon and Ghana. Each cluster’s population wealth is objectively ranked based on variables known to be associated with poverty: mean cluster child height-for-age Z-score (HAZ) and child mortality. In both countries, the efficient EconomicClusters model accounted for a similar proportion of the variance in child HAZ and child mortality as the >25 variable DHS Wealth Index model. This data-adaptive SES metric derived from publicly available DHS surveys selects the most appropriate variables for each country, minimizing resources needed for longitudinal health equity surveillance.
Our overall objective will be met through the following Specific Aims:
Aim 1. Develop EconomicClusters models for 37 sub-Saharan African countries with Demographic and Health Survey (DHS) data since 2010
Aim 2: Validate the EconomicClusters models for each country using established DHS metrics known to be consistently correlated with socioeconomic status: proportion of a woman’s children who are deceased, whether healthcare is sought at public or private facilities, and educational attainment.
Aim 3: Characterize inequity in trauma care access and outcomes in Cameroon, South Africa, and Uganda by implementing the EconomicClusters strategy in trauma registry data collection.
Aim 4: Develop a free, publicly available toolkit to support researchers to use EconomicClusters models strategy in their own research.