Geospatial patterns of multiple environmental exposures and socioeconomic status in Australian cities

Background: The urban built environment influences population health. Environmental inequity occurs when features of the built environment are unequally distributed in cities, which may lead to health disparities between socioeconomically advantaged and disadvantaged communities. Urban planning and population health policy can benefit from an improved understanding of spatial distributions and patterns of environmental exposures. Methods: We calculated prevalence ratios of the lowest and highest tertiles of six environmental exposures (walkability, blue space, tree cover, parklands, nitrogen dioxide, and major road density) within quintiles of socioeconomic advantage and disadvantage for Adelaide, Sydney, and Brisbane, Australia. We included 550 neighbourhoods, covering 92.5–97.4 % of the city populations. We overlaid tertiles of each environmental exposure to create combinations of health-promoting environmental variables (defined as ‘sweet’) and health-harming environmental variables (defined as ‘sour’). Maps were created using the tertile combinations. Results: Walkability was more prevalent in socioeconomically advantaged areas in each city (e.g., high walkability was 23–64 % more prevalent in the highest socioeconomic quintiles compared to city-wide prevalence in all cities). This was also the case for high tree cover in Adelaide (130 % higher prevalence) and Sydney (74 % higher prevalence). In contrast, low socioeconomic areas in each city had lower prevalence of high tree cover. However, we did observe high prevalence of undesirable environmental exposures in the most advantaged neighbourhoods (e.g., 82 % higher prevalence of nitrogen dioxide in Brisbane's highest socioeconomic quintile compared to city-wide prevalence). Although only a small percentage of neighbourhoods were considered ‘sweet’ or ‘sour’, we generally found that ‘sour’ areas were located towards the city outskirts, whilst ‘sweet’ areas were more common in the inner-city. Conclusions: By creating visual maps and assessing the prevalence of environmental exposures across a socioeconomic gradient, we were able to identify areas where there are inequities in health-promoting or health-harming urban environments. These areas could be targeted for urban interventions to improve liveability.