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. 2008 May 1:7:20.
doi: 10.1186/1476-072X-7-20.

Linking stroke mortality with air pollution, income, and greenness in northwest Florida: an ecological geographical study

Zhiyong Hu  1 , Johan Liebens, K Ranga Rao
Affiliations

Linking stroke mortality with air pollution, income, and greenness in northwest Florida: an ecological geographical study

Zhiyong Hu et al. Int J Health Geogr. .

Abstract

Background: Relatively few studies have examined the association between air pollution and stroke mortality. Inconsistent and inclusive results from existing studies on air pollution and stroke justify the need to continue to investigate the linkage between stroke and air pollution. No studies have been done to investigate the association between stroke and greenness. The objective of this study was to examine if there is association of stroke with air pollution, income and greenness in northwest Florida.

Results: Our study used an ecological geographical approach and dasymetric mapping technique. We adopted a Bayesian hierarchical model with a convolution prior considering five census tract specific covariates. A 95% credible set which defines an interval having a 0.95 posterior probability of containing the parameter for each covariate was calculated from Markov Chain Monte Carlo simulations. The 95% credible sets are (-0.286, -0.097) for household income, (0.034, 0.144) for traffic air pollution effect, (0.419, 1.495) for emission density of monitored point source polluters, (0.413, 1.522) for simple point density of point source polluters without emission data, and (-0.289,-0.031) for greenness. Household income and greenness show negative effects (the posterior densities primarily cover negative values). Air pollution covariates have positive effects (the 95% credible sets cover positive values).

Conclusion: High risk of stroke mortality was found in areas with low income level, high air pollution level, and low level of exposure to green space.

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Figures

Figure 1
Figure 1
Study area.
Figure 2
Figure 2
Choropleth mapping vs. dasymetric mapping for environment exposure calculation. Left: Map of an environment exposure (area within the green boundary represents human activity area and the whole square grid represents a census unit). Right: Cell values of the environment exposure (red represents human activity area). The mean exposure value is 87 for red cells, and 77 for the whole census unit.
Figure 3
Figure 3
Maps of point source polluters.
Figure 4
Figure 4
Annual average daily traffic count.
Figure 5
Figure 5
How kernel density for line features works. The illustration shows a line segment and the kernel surface fitted over it. The contribution of the line segment to density is equal to the value of the kernel surface at the raster cell center.
Figure 6
Figure 6
Raster surface of kernel density of annual average daily traffic count.
Figure 7
Figure 7
Dasymetric map of SMRs.
Figure 8
Figure 8
Dasymetric map of household income.
Figure 9
Figure 9
Dasymetric map of annual average daily traffic density.
Figure 10
Figure 10
Dasymetric map of emission density of monitored point source polluters.
Figure 11
Figure 11
Dasymetric map of density of point source polluters without emission data.
Figure 12
Figure 12
Dasymetric map of greenness.
Figure 13
Figure 13
Trace plots of the 10,000 Markov Chain Monte Carlo (MCMC) updates. Simulation trace plots for the intercept, income effect, traffic air pollution effect, effect of EPA and Florida DEP monitored point source air emission, effect of non-monitored point source air pollution, and greenness effect for the Bayesian hierarchical model with a convolution prior. Horizontal axis represents iteration number and vertical axis represents simulated parameter value. The red trace is for one Markov chain, and the blue for the other.
Figure 14
Figure 14
Kernel estimates of the posterior densities of the fixed effects in the Bayesian hierarchical model. Horizontal axis represents simulated parameter values and vertical axis represents the density of each value.
Figure 15
Figure 15
Model simulated standardized mortality rates (relative risks).
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