Relative residential property value as a socio-economic status indicator for health research
1 Social Epidemiology and Evaluation Research Group, School of Population Health & Sansom Institute for Health Research, Division of Health Sciences, GPO Box 2471, City East Campus, University of South Australia, Adelaide 5001, Australia
2 Discipline of Geography, Environment and Population and the Australian Population and Migration Research Centre, The University of Adelaide, North Terrace Campus, Adelaide 5000, Australia
3 Centre for Regulation and Market Analysis, UniSA School of Business, University of South Australia, City West Campus, GPO Box 2471, Adelaide 5001, Australia
4 Research Centre of the Douglas Mental Health University Institute, 6875 LaSalle Boulevard Montreal, Quebec H4H 1R3, Canada
5 Department of Medicine, The University of Melbourne, St Vincent's Hospital, Clinical Sciences Building 29 Regent Street, Fitzroy, Melbourne, VIC 3065, Australia
International Journal of Health Geographics 2013, 12:22 doi:10.1186/1476-072X-12-22Published: 15 April 2013
Residential property is reported as the most valuable asset people will own and therefore provides the potential to be used as a socio-economic status (SES) measure. Location is generally recognised as the most important determinant of residential property value.
Extending the well-established relationship between poor health and socio-economic disadvantage and the role of residential property in the overall wealth of individuals, this study tested the predictive value of the Relative Location Factor (RLF), a SES measure designed to reflect the relationship between location and residential property value, and six cardiometabolic disease risk factors, central obesity, hypertriglyceridemia, reduced high density lipoprotein (HDL), hypertension, impaired fasting glucose, and high low density lipoprotein (LDL). These risk factors were also summed and expressed as a cumulative cardiometabolic risk (CMR) score.
RLF was calculated using a global hedonic regression model from residential property sales transaction data based upon several residential property characteristics, but deliberately blind to location, to predict the selling price of the property. The predicted selling price was divided by the actual selling price and the results interpolated across the study area and classified as tertiles. The measures used to calculate CMR were collected via clinic visits from a population-based cohort study. Models with individual risk factors and the cumulative cardiometabolic risk (CMR) score as dependent variables were respectively tested using log binomial and Poisson generalised linear models.
A statistically significant relationship was found between RLF, the cumulative CMR score and all but one of the risk factors. In all cases, participants in the most advantaged and intermediate group had a lower risk for cardio-metabolic diseases. For the CMR score the RR for the most advantaged was 19% lower (RR = 0.81; CI 0.76-0.86; p <0.0001) and the middle group was 9% lower (RR = 0.91; CI 0.86-0.95; p <0.0001) than the least advantaged group.
This paper advances the understanding of the nexus between place, health and SES by providing an objective spatially informed SES measure for testing health outcomes and reported a robust association between RLF and several health measures.