OP69 Do ‘environmental bads’ such as alcohol, fast food, tobacco, and gambling outlets cluster and co-locate in more deprived areas in glasgow city, scotland?

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Abstract

Background

Recent research on the determinants of health-related behaviours, such as smoking, heavy drinking and poor diet, has begun to focus on physical environmental factors, such as the retail environment, and associations with area level deprivation. This study utilises an innovative application of spatial cluster analysis to examine the socio-spatial patterning of various categories of outlets, selling potentially health-damaging goods/services (alcohol, fast food, tobacco and gambling) within Glasgow. This novel application advances existing methods for quantifying spatial access to retail outlets as it is not restricted by pre-defined boundaries.

Methods

Outlet address data was obtained from Glasgow City Council for 2012 (tobacco, fast food), and 2013 (alcohol, gambling) and mapped using GIS software. SaTScan, a well-established cluster analysis tool, was used to detect spatial clusters of outlets and ascertain their statistical significance (at the 5% level). Analysis was performed for all categories of outlets combined (to examine co-location), and individually for alcohol, fast food, tobacco, and gambling outlets. Software provided output for clusters centroids, size (radius) and statistical significance. Clusters were assigned a Scottish Index of Multiple Deprivation 2012 Income score; quintiles of income deprivation were calculated from 1 (most deprived) to 5 (least deprived) and compared for numbers of clusters.

Results

Across the city, there were 28 areas where all four types of outlets were co-located; and for individual outlets, there were 20 alcohol outlet clusters, 16 fast food outlet clusters, 15 tobacco outlet clusters and 5 gambling outlet clusters. Co-occurrence clusters were more common in deprived areas, with ten clusters in the more deprived quintile compared to one in the most affluent quintile. In terms of individual categories of outlet, poorer areas contained the largest number of alcohol, fast food, tobacco and gambling outlet clusters. Co-location of individual types of outlets in similar geographical areas was also evident, for example: located in the central business district, other retail, office, service hubs, and also deprived areas in the ‘east end’.

Conclusion

The study makes use of a robust technique to detect clusters and adds to evidence that deprived areas have increased access to potentially health damaging goods/services. Such research can inform interventions to tackle the co-occurrence of health behaviours, and findings could aid authorities to develop policy/planning regulations appropriate for areas in greatest need.

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