Project 1 Equity Surveillance
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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
Researchers studying health disparities can use Economic Clusters to track SES with population-specific economic models that perform as well as the DHS Wealth Index, but are more feasible in time-constrained settings.
A free toolkit with variables and corresponding algorithms will be shared through a free platform for use by investigators interested in health equity research.
This Project shall be led by the following Principal Investigators and co-investigators.