Environmental influences associated with gambling in young adulthood

J Urban Health. 2013 Feb;90(1):130-40. doi: 10.1007/s11524-012-9751-1.

Abstract

Social and environmental influences on gambling behavior are important to understand because localities can control the sanction and location of gambling opportunities. This study explores whether neighborhood disadvantage is associated with gambling among predominantly low-income, urban young adults and to explore if we can find differences in physical vs. compositional aspects of the neighborhood. Data are from a sample of 596 young adults interviewed when they were 21-22 years, who have been participating in a longitudinal study since entering first grade in nine public US Mid-Atlantic inner-city schools (88 % African Americans). Data were analyzed via factor analysis and logistic regression models. One third of the sample (n = 187) were past-year gamblers, 42 % of them gambled more than once a week, and 31 % had gambling-related problems. Those living in moderate and high disadvantaged neighborhoods were significantly more likely to be past-year gamblers than those living in low disadvantaged neighborhoods. Those living in high disadvantaged neighborhoods were ten times more likely than those living in low disadvantaged neighborhoods to have gambling problems. Factor analysis yielded a 2-factor model, an "inhabitant disadvantage factor" and a "surroundings disadvantage factor." Nearly 60 % of the sample lived in neighborhoods with high inhabitants disadvantage (n = 375) or high surroundings disadvantage (n = 356). High inhabitants disadvantage was associated with past-year frequent gambling (odds ratios (aOR) = 2.26 (1.01, 5.02)) and gambling problems (aOR = 2.81 (1.18, 6.69)). Higher neighborhood disadvantage, particularly aspects of the neighborhood concerning the inhabitants, was associated with gambling frequency and problems among young adult gamblers from an urban, low-income setting.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Factor Analysis, Statistical
  • Gambling / epidemiology*
  • Humans
  • Logistic Models
  • Poverty / statistics & numerical data
  • United States / epidemiology
  • Urban Population / statistics & numerical data