title: Identifying food deserts and swamps based on relative healthy food access: a spatio-temporal Bayesian approach.
abstract: BACKGROUND
  Obesity and other adverse health outcomes are influenced by individual- and neighbourhood-scale risk factors, including the food environment. At the small-area scale, past research has analysed spatial patterns of food environments for one time period, overlooking how food environments change over time. Further, past research has infrequently analysed relative healthy food access (RHFA), a measure that is more representative of food purchasing and consumption behaviours than absolute outlet density.
  
  
  METHODS
  This research applies a Bayesian hierarchical model to analyse the spatio-temporal patterns of RHFA in the Region of Waterloo, Canada, from 2011 to 2014 at the small-area level. RHFA is calculated as the proportion of healthy food outlets (healthy outlets/healthy + unhealthy outlets) within 4-km from each small-area. This model measures spatial autocorrelation of RHFA, temporal trend of RHFA for the study region, and spatio-temporal trends of RHFA for small-areas.
  
  
  RESULTS
  For the study region, a significant decreasing trend in RHFA is observed (-0.024), suggesting that food swamps have become more prevalent during the study period. For small-areas, significant decreasing temporal trends in RHFA were observed for all small-areas. Specific small-areas located in south Waterloo, north Kitchener, and southeast Cambridge exhibited the steepest decreasing spatio-temporal trends and are classified as spatio-temporal food swamps.
  
  
  CONCLUSIONS
  This research demonstrates a Bayesian spatio-temporal modelling approach to analyse RHFA at the small-area scale. Results suggest that food swamps are more prevalent than food deserts in the Region of Waterloo. Analysing spatio-temporal trends of RHFA improves understanding of local food environment, highlighting specific small-areas where policies should be targeted to increase RHFA and reduce risk factors of adverse health outcomes such as obesity.
authors.affiliation: Faculty of Environment, School of Planning, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada. h3luan@uwaterloo.ca.
authors.forename: Hui
authors.initials: H
authors.lastname: Luan
authors.affiliation: Faculty of Environment, School of Planning, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada. j9law@uwaterloo.ca.|Faculty of Applied Health Sciences, School of Public Health and Health System, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada. j9law@uwaterloo.ca.
authors.forename: Jane
authors.initials: J
authors.lastname: Law
authors.affiliation: Faculty of Environment, School of Planning, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada. mquick@uwaterloo.ca.
authors.forename: Matthew
authors.initials: M
authors.lastname: Quick
mesh_terms: D001499:Bayes Theorem; D004777:Environment; D005523:Food Supply / Q000145:classification* / Q000592:standards / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D009765:Obesity / Q000209:etiology / Q000517:prevention & control; D009864:Ontario; D011156:Population Density; D012111:Residence Characteristics / Q000706:statistics & numerical data*; D012147:Restaurants / Q000592:standards / Q000706:statistics & numerical data*; D012307:Risk Factors; D017062:Small-Area Analysis; D062211:Spatio-Temporal Analysis