title: Alcohol outlets and binge drinking in urban neighborhoods: the implications of nonlinearity for intervention and policy.
abstract: OBJECTIVE
  Alcohol outlet density has long been associated with alcohol-related harms, and policymakers have endorsed alcohol outlet restriction to reduce these harms. However, potential nonlinearity in the relation between outlet density and alcohol consumption has not been rigorously examined.
  
  
  METHODS
  We used data from the New York Social Environment Study (n = 4000) to examine the shape of the relation between neighborhood alcohol outlet density and binge drinking by using a generalized additive model with locally weighted scatterplot smoothing, and applied an imputation-based marginal modeling approach.
  
  
  RESULTS
  We found a nonlinear relation between alcohol outlet density and binge drinking; the association was stronger at densities of more than 80 outlets per square mile. Binge drinking prevalence was estimated to be 13% at 130 outlets, 8% at 80 outlets, and 8% at 20 outlets per square mile.
  
  
  CONCLUSIONS
  This nonlinearity suggests that reductions in alcohol outlet density where density is highest and the association is strongest may have the largest public health impact per unit reduction. Future research should assess the impact of policies and interventions that aim to reduce alcohol outlet density, and consider nonlinearity in effects.
authors: Division of Epidemiology, University of California, Berkeley School of Public Health, Berkeley, CA 94720-7358, USA. jahern@berkeley.edu
authors: Jennifer
authors: J
authors: Ahern
authors: Claire
authors: C
authors: Margerison-Zilko
authors: Alan
authors: A
authors: Hubbard
authors: Sandro
authors: S
authors: Galea
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000434:Alcoholic Beverages / Q000600:supply & distribution*; D063425:Binge Drinking / Q000453:epidemiology*; D003132:Commerce / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009519:New York City / Q000453:epidemiology; D015995:Prevalence; D012111:Residence Characteristics; D012307:Risk Factors; D014505:Urban Population / Q000706:statistics & numerical data*