Using GIS-Based Methods of Multicriteria Analysis to Construct Socio-Economic Deprivation Indices

Background:Over the past several decades researchers have produced substantial evidence of asocial gradient in a variety of health outcomes, rising from systematic differences in income,education, employment conditions, and family dynamics within the population. Social gradients inhealth are measured using deprivation indices, which are typically constructed from aggregatedsocio-economic data taken from the national census – a technique which dates back at least untilthe early 1970's. The primary method of index construction over the last decade has been aPrincipal Component Analysis. Seldom are the indices constructed from survey-based data sourcesdue to the inherent difficulty in validating the subjectivity of the response scores. We argue thatthis very subjectivity can uncover spatial distributions of local health outcomes. Moreover,indication of neighbourhood socio-economic status may go underrepresented when weightedwithout expert opinion. In this paper we propose the use of geographic information science (GIS)for constructing the index. We employ a GIS-based Order Weighted Average (OWA) MulticriteriaAnalysis (MCA) as a technique to validate deprivation indices that are constructed using morequalitative data sources. Both OWA and traditional MCA are well known and used methodologiesin spatial analysis but have had little application in social epidemiology.Results:A survey of British Columbia's Medical Health Officers (MHOs) was used to populate theMCA-based index. Seven variables were selected and weighted based on the survey results. OWAvariable weights assign both local and global weights to the index variables using a sliding scale,producing a range of variable scenarios. The local weights also provide leverage for controlling thelevel of uncertainty in the MHO response scores. This is distinct from traditional deprivationindices in that the weighting is simultaneously dictated by the original respondent scores and thevalue of the variables in the dataset.Conclusion:OWA-based MCA is a sensitive instrument that permits incorporation of expertopinion in quantifying socio-economic gradients in health status. OWA applies both subjective andobjective weights to the index variables, thus providing a more rational means of incorporatingsurvey results into spatial analysis.