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BackgroundObesity 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.MethodsThis 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.ResultsFor 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.ConclusionsThis 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.

Background and Purpose-Current evidence suggests that stroke mortality and hospital admissions should be higher in areas with elevated levels of outdoor air pollution because of the combined acute and chronic exposure effects of air pollution. We examined this hypothesis using a small-area level ecological correlation study. Methods-We used 1030 census enumeration districts as the unit of analysis and examined stroke deaths and hospital admissions from 1994 to 1998, with census denominator counts for people Ն45 years. Modeled air pollution data for particulate matter (PM 10 ), nitrogen oxides (NO x ), and carbon monoxide (CO) were interpolated to census enumeration districts. We adjusted for age, sex, socioeconomic deprivation, and smoking prevalence. Results-The analysis was based on 2979 deaths, 5122 admissions, and a population of 199 682. After adjustment for potential confounders, stroke mortality was 37% (95% CI, 19 to 57), 33% (95% CI, 14 to 56), and 26% (95% CI, 10 to 46) higher in the highest, relative to the lowest, NO x , PM 10 , and CO quintile categories, respectively. Corresponding increases in risk for admissions were 13% (95% CI, 1 to 27), 13% (95% CI, Ϫ1 to 29), and 11% (95% CI, Ϫ1 to 25). Conclusion-The results are consistent with an excess risk of stroke mortality and, to a lesser extent, hospital admissions in areas with high outdoor air pollution levels. If causality were assumed, 11% of stroke deaths would have been attributable to outdoor air pollution. Targeting policy interventions at high pollution areas may be a feasible option for stroke prevention.

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The results are consistent with an excess risk of coronary heart disease mortality in areas with high outdoor NO(x), a proxy for traffic-related pollution, but residual confounding cannot be ruled out. If causality were assumed, 6% of coronary heart disease deaths would have been attributable to outdoor NO(x,) and targeting pollution reduction measures at high pollution areas would be an option for coronary mortality prevention.

We model the relationship between coronary heart disease and smoking prevalence and deprivation at the small area level using the Poisson log-linear model with and without random effects. Extra-Poisson variability (overdispersion) is handled through the addition of spatially structured and unstructured random effects in a Bayesian framework. In addition, four different measures of smoking prevalence are assessed because the smoking data are obtained from a survey that resulted in quite large differences in the size of the sample across the census tracts. Two of the methods use Bayes adjustments of standardized smoking ratios (local and global adjustments), and one uses a nonparametric spatial averaging technique. A preferred model is identified based on the deviance information criterion. Both smoking and deprivation are found to be statistically significant risk factors, but the effect of the smoking variable is reduced once the confounding effects of deprivation are taken into account. Maps of the spatial variability in relative risk, and the importance of the underlying covariates and random effects terms, are produced. We also identify areas with excess relative risk.