Derivation of indices of socioeconomic status for health services research in Asia

Abstract

Background: Environmental contexts have been shown to predict health behaviours and outcomes either directly or via interaction with individual risk factors. In this paper, we created indexes of socioeconomic disadvantage (SEDI) and socioeconomic advantage (SAI) in Singapore to test the applicability of these concepts in an Asian context. These indices can be used for health service resource allocation, research and advocacy. Methods: We used principal component analysis (PCA) to create SEDI and SAI using a structured and iterative process to identify and include influential variables in the final index. Data at the master plan geographical level was obtained from the most recent Singapore census 2010. Results: The 3 areas with highest SEDI scores were Outram (120.1), followed by Rochor (111.0) and Downtown Core (110.4). The areas with highest SAI scores were Tanglin, River Valley and Newton. The SAI had 89.6% of variation explained by the final model, as compared to 67.1% for SEDI, and we recommend using both indices in any analysis. Conclusion: These indices may prove useful for policy-makers to identify spatially varying risk factors, and in turn help identify geographically targeted intervention programs, which can be more cost effective to conduct.

Keywords

• Principal component analysis
• Socio-economic index
• Small area health outcomes
• Policy
• Health care utilization
• Asia