Sunday, January 15, 2012: 8:45 AM
Roosevelt (Grand Hyatt Washington)
* noted as presenting author
Background and Purpose: Neighborhood-level geographical factors, such as density of alcohol outlets and neighborhood disadvantage, play an important role in substance abuse epidemiology. Although the use of Geographical Information Systems (GIS) to measure substance abuse risk has gained momentum in the past 10 years, research on the relationship between geographical factors and treatment outcomes is limited. There is a need for research in the substance abuse field that accounts for areal influences (i.e., neighborhood and physical environments) on substance abuse treatment outcomes. The objective of this study was to examine factors that may play a role in substance abuse treatment outcomes using spatial and local geographical modeling. Landscapes of areal socioeconomic, demographic, and physical factors were created from U.S. Census and local physical data. Using these landscapes, a risk assessment was conducted to determine the distribution of risk for negative treatment outcomes in Buffalo, New York. Methods: Neighborhood landscapes in Buffalo, NY were modeled using US Census 2000 data. Census-tract boundaries were used to define the study area. Neighborhood-level demographic data like population, area, employment, age, and income-based variables were also used. Point data included locations of substance use treatment centers from the Substance Abuse and Mental Health Services Administration (SAMHSA) as well as alcohol outlets (i.e., liquor stores, restaurants, mini-markets) in Buffalo, NY (n=620) from the New York State Liquor Authority Division of Alcoholic Beverage Control. Risk assessment is a quantitative analytical technique for understanding the vulnerability and hazards involved in a natural event. In this study, vulnerability was made up of factors (e.g., SES, high density of alcohol outlets) that may affect the event of interest (i.e., SA Tx outcome); each factor contributed its own score toward overall risk. This study generalized individual-level factors from the Expected Treatment Outcome Scale (ETOS) to the neighborhood level and intersected them with measures of neighborhood disadvantage to create a socioeconomic landscape. Results: Areas of highest risk for negative treatment outcomes were geographically linked to areas that were low in socioeconomic status, high density of alcohol outlets, and low density of treatment centers. An examination of the difference between total risk and the layers of socioeconomic and physical environment vulnerability showed distinct areas where the calculated total risk was higher than each layer alone, which highlights areas of elevated risk in each landscape. The areas of difference were much larger than the regions of highest risk in each individual layer suggesting that these areas may be subject to higher risk of negative treatment outcomes. Areas of maximum risk are identified. Conclusion and implications: This study is an example of how GIS may be used to identify risk and target treatment intervention strategies to consumers in a specific geographical context. Findings allow for the identification of factors that may mitigate risk in certain areas. Results from this model will help local treatment providers define where core areas of risk occur.
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