Measuring Alcohol Outlet Density: An Overview of Strategies for Public Health Practitioners

Abstract

Context: Excessive alcohol use is responsible for 88 000 deaths in the United States annually and cost the United States $249 billion in 2010. There is strong scientific evidence that regulating alcohol outlet density is an effective intervention for reducing excessive alcohol consumption and related harms, but there is no standard method for measuring this exposure.

Program: We overview the strategies available for measuring outlet density, discuss their advantages and disadvantages, and provide examples of how they can be applied in practice.

Implementation: The 3 main approaches for measuring density are container-based (eg, number of outlets in a county), distance-based (eg, average distance between a college and outlets), and spatial access-based (eg, weighted distance between town center and outlets).

Evaluation: While container-based measures are the simplest to calculate and most intuitive, distance-based or spatial access-based measures are unconstrained by geopolitical boundaries and allow for assessment of clustering (an amplifier of certain alcohol-related harms). Spatial access-based measures can also be adjusted for population size/demographics but are the most resource-intensive to produce.

Discussion: Alcohol outlet density varies widely across and between locations and over time, which is why it is important to measure it. Routine public health surveillance of alcohol outlet density is important to identify problem areas and detect emerging ones. Distance- or spatial access-based measures of alcohol outlet density are more resource-intensive than container-based measures but provide a much more accurate assessment of exposure to alcohol outlets and can be used to assess clustering, which is particularly important when assessing the relationship between density and alcohol-related harms, such as violent crime.

FIGURE 1

Overview of Approaches to Measuring Alcohol Outlet Density ^aContainer can be predefined (eg, geopolitical: county, city, census block) or user-defined (buffer zone or ad hoc). A user-defined buffer zone can be specified through any of the following 3 approaches: (a) Euclidean; (b) street network distance; or (c) driving time. User specifies the values to be used for the chosen approach (eg, “1-mile Euclidean distance”; “1-mile driving distance”; or “5-minute driving time”); in turn, these values define the container's shape and size. An ad hoc container can be defined using standard geopolitical building blocks (eg, groupings of census tracts or neighborhoods). ^bDistance can be thought of as either “spatial distance” or “time.” It can be determined through any of the following 3 approaches: (a) Euclidean—or “crows' flight”—distance; (b) street network—or “Manhattan”—distance; or (c) driving time, which accounts for street networks and traffic speeds. The user specifies the reference value(s) for the chosen approach (eg, “1-mile Euclidean distance”; “1-mile driving distance”; or “5-minute driving time”). The distance reflects the economic/convenience cost of access. ^cReference point can be any user-specified point, such as (a) a street address or point of interest (eg, school, household); (b) geometric or population-weighted centroid (of a county, census tract, block group, etc); or (c) other alcohol outlet. ^dDenominators for rates can be any of the following 3 choices: (a) population within the container; (b) total land area within the container; or (c) road miles within the container. “Container” can be either predefined or user-defined.

FIGURE 2

Comparison of Distance-Based and Spatial Access–Based Measures of Distance (in Meters) for Assessing Alcohol Outlet Density ^aThis example demonstrates how a spatial access–based measure of distance can better reflect proximity to alcohol outlets than a distance-based measure. Closer proximity to alcohol outlets is associated with an increased risk of alcohol-attributable harms (eg, violent crime).