Using Principal Component Analysis to Identify Priority Neighbourhoods for Health Services Delivery by Ranking Socioeconomic Status.

Abstract

Objectives. Socioeconomic status (SES) is a comprehensive indicator of health status and is useful in area-level health research and informing public health resource allocation. Principal component analysis (PCA) is a useful tool for developing SES indices to identify area-level disparities in SES within communities. While SES research in Canada has relied on census data, the voluntary nature of the 2011 National Household Survey challenges the validity of its data, especially income variables. This study sought to determine the appropriateness of replacing census income information with tax filer data in neighbourhood SES index development. Methods. Census and taxfiler data for Guelph, Ontario were retrieved for the years 2005, 2006, and 2011. Data were extracted for eleven income and non-income SES variables. PCA was employed to identify significant principal components from each dataset and weights of each contributing variable. Variable-specific factor scores were applied to standardized census and taxfiler data values to produce SES scores. Results. The substitution of taxfiler income variables for census income variables yielded SES score distributions and neighbourhood SES classifications that were similar to SES scores calculated using entirely census variables. Combining taxfiler income variables with census non-income variables also produced clearer SES level distinctions. Internal validation procedures indicated that utilizing multiple principal components produced clearer SES level distinctions than using only the first principal component. Conclusion. Identifying socioeconomic disparities between neighbourhoods is an important step in assessing the level of disadvantage of communities. The ability to replace census income information with taxfiler data to develop SES indices expands the versatility of public health research and planning in Canada, as more data sources can be explored. The apparent usefulness of PCA also contributes to the improvement of SES measurement and calculation methods, and the freedom to input area-specific data allows the present method to be adapted to other locales.