Christopher Morrison1. 1. Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Vic., Australia; Prevention Research Center, Pacific Institute for Research and Evaluation, Oakland, California.
Abstract
BACKGROUND: Lower-income populations are exposed to excess risks related to the presence of greater concentrations of alcohol outlets in their communities. Theory from economic geography suggests this is due to dynamic processes that shape urban retail markets (as outlets are attracted to areas of higher population density due to the increased demand but are excluded from higher-income areas due to land and structure rents). This mechanism may explain increased exposure to alcohol outlets for lower-income populations in rural areas. This study tests the hypothesis that the distribution of outlets between rural towns will reflect these market dynamics, such that outlets are concentrated in towns with (i) greater resident and temporary populations, (ii) with lower income, and (iii) which are adjacent to towns with higher income. METHOD: Bayesian conditional autoregressive Poisson models examined counts of bars, restaurants, and off-premise outlets within 353 discrete towns of rural Victoria, Australia (mean population = 4,236.0, SD = 15,754.1). Independent variables were each town's total resident population, net changes to population (due to commuter flow, visitors, and the flow of local residents to other towns [spatial interaction]), and income for the local and adjacent towns. RESULTS: Lower local income and increased income in adjacent towns were associated with more outlets of all types. Greater resident populations and greater net population due to commuters also predicted greater numbers of all outlets. Bars and restaurants were positively related to greater net population due to visitors and negatively related to spatial interaction. CONCLUSIONS: The economic geographic processes that lead to greater concentrations of alcohol outlets in lower-income areas are common to all retail markets. Lower-income populations are exposed to increased risk associated with the presence of additional outlets that service demand from nonresidents. In rural areas, these processes appear to operate between discrete towns.
BACKGROUND: Lower-income populations are exposed to excess risks related to the presence of greater concentrations of alcohol outlets in their communities. Theory from economic geography suggests this is due to dynamic processes that shape urban retail markets (as outlets are attracted to areas of higher population density due to the increased demand but are excluded from higher-income areas due to land and structure rents). This mechanism may explain increased exposure to alcohol outlets for lower-income populations in rural areas. This study tests the hypothesis that the distribution of outlets between rural towns will reflect these market dynamics, such that outlets are concentrated in towns with (i) greater resident and temporary populations, (ii) with lower income, and (iii) which are adjacent to towns with higher income. METHOD: Bayesian conditional autoregressive Poisson models examined counts of bars, restaurants, and off-premise outlets within 353 discrete towns of rural Victoria, Australia (mean population = 4,236.0, SD = 15,754.1). Independent variables were each town's total resident population, net changes to population (due to commuter flow, visitors, and the flow of local residents to other towns [spatial interaction]), and income for the local and adjacent towns. RESULTS: Lower local income and increased income in adjacent towns were associated with more outlets of all types. Greater resident populations and greater net population due to commuters also predicted greater numbers of all outlets. Bars and restaurants were positively related to greater net population due to visitors and negatively related to spatial interaction. CONCLUSIONS: The economic geographic processes that lead to greater concentrations of alcohol outlets in lower-income areas are common to all retail markets. Lower-income populations are exposed to increased risk associated with the presence of additional outlets that service demand from nonresidents. In rural areas, these processes appear to operate between discrete towns.
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