Poster Presentation Australian & New Zealand Obesity Society 2014 Annual Scientific Meeting

Change over time in wealth approximated by relative residential location factor is associated with changes over time in body mass index and waist circumference (#236)

Neil Coffee 1 , Natasha Howard 1 , Catherine Paquet 1 , Anne Taylor 2 , Robert Adams 3 , Graeme Hugo 4 , Mark Daniel 1 , Theo Niyonsenga 1
  1. University of South Australia School of Population, Adelaide, SA, Australia
  2. Population Research and Outcome Studies, Discipline of Medicine, The University of Adelaide, Adelaide, South Australia, Australia
  3. The Health Observatory, The Queen Elizabeth Hospital Campus, The University of Adelaide, Adelaide, South Australia, Australia
  4. Discipline of Geography, Environment and Population, The University of Adelaide, Adelaide, South Australia, Australia

Background: Obesity remains a pressing public health problem. Globally, 37% of men and 38% of women are overweight or obese.  Over the last three decades, Australasia has outpaced other regions of the world with the largest absolute increase in adult obesity. Obesity is inversely associated with measures of socioeconomic status (SES) including education, or income. Fewer studies have its relationship to wealth. In cross-sectional population studies, a recently-developed, novel spatial property wealth indicator, relative location factor (RLF) was shown to be associated with body mass index (BMI) and waist circumference (WAISTC). This 10-year longitudinal study evaluated whether RLF trajectory was associated with trajectories of BMI and WAISTC.

Methods: Trajectories of BMI, WAISTC and RLF were determined for participants in a population cohort (n=4056 at baseline) collected across three waves between 2000 and 2010. RLF was derived from a hedonic regression model including selected residential property characteristics. Multivariate latent growth curve models were fitted to the data to estimate associations between change RLF and changes in BMI and WAISTC accounting for individual age, gender and education, and area-level SES.

Findings: BMI, WAISTC and RLF increased over time. Average growths and variations across individuals were statistically significant. As RLF rose over time, BMI and WAISTC decreased, that is, positive growth in RLF was associated with reduced growth in BMI and WAISTC. Statistically significant associations between growth intercepts remained even when accounting for individual- and area-level covariates.

Conclusion: Longitudinal analysis using growth models enabled the estimation of individual trajectories and to examine the extent to which they are associated to each other. Although rising RLF was associated with reduced growth in BMI and WAISTC, other factors such as diet and active lifestyle could contribute to changes in BMI and WAISTC.

  1. Neil Coffee, Tony Lockwood, Graeme Hugo, Catherine Paquet, Natasha Howard and Mark Daniel. Relative residential property value as a socio-economic status indicator for health research. International Journal of Health Geographics 2013, 12:22
  2. Khoo, S.T. & Muthén, B. (2000). Longitudinal data on families: Growth modeling alternatives. Multivariate Applications in Substance use Research, J. Rose, L. Chassin, C. Presson & J. Sherman (eds.), Hillsdale, N.J.: Erlbaum, pp. 43-78
  3. Ngo A, Paquet C, Howard NJ, Coffee NT, Taylor AW, Adams RJ, Daniel M (2014). Area-level socioeconomic characteristics, prevalence and trajectories of cardiometabolic risk. International Journal of Environmental Research and Public Health; 11(1): 830-848