Journal of studies on alcohol and drugs · 2010
Drinking, driving, and crashing: a traffic-flow model of alcohol-related motor vehicle accidents.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод пока не подготовлен
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Gruenewald Paul J, Johnson Fred W
Аннотация
OBJECTIVE: This study examined the influence of on-premise alcohol-outlet densities and of drinking-driver densities on rates of alcohol-related motor vehicle crashes. A traffic-flow model is developed to represent geographic relationships between residential locations of drinking drivers, alcohol outlets, and alcohol-related motor vehicle crashes.
METHOD: Cross-sectional and time-series cross-sectional spatial analyses were performed using data collected from 144 geographic units over 4 years. Data were obtained from archival and survey sources in six communities. Archival data were obtained within community areas and measured activities of either the resident population or persons visiting these communities. These data included local and highway traffic flow, locations of alcohol outlets, population density, network density of the local roadway system, and single-vehicle nighttime (SVN) crashes. Telephone-survey data obtained from residents of the communities were used to estimate the size of the resident drinking and driving population.
RESULTS: Cross-sectional analyses showed that effects relating on-premise densities to alcohol-related crashes were moderated by highway trafficflow. Depending on levels of highway traffic flow, 10% greater densities were related to 0% to 150% greater rates of SVN crashes. Time-series cross-sectional analyses showed that changes in the population pool of drinking drivers and on-premise densities interacted to increase SVN crash rates.
CONCLUSIONS: A simple traffic-flow model can assess the effects of on-premise alcohol-outlet densities and of drinking-driver densities as they vary across communities to produce alcohol-related crashes. Analyses based on these models can usefully guide policy decisions on the sitting of on-premise alcohol outlets.
METHOD: Cross-sectional and time-series cross-sectional spatial analyses were performed using data collected from 144 geographic units over 4 years. Data were obtained from archival and survey sources in six communities. Archival data were obtained within community areas and measured activities of either the resident population or persons visiting these communities. These data included local and highway traffic flow, locations of alcohol outlets, population density, network density of the local roadway system, and single-vehicle nighttime (SVN) crashes. Telephone-survey data obtained from residents of the communities were used to estimate the size of the resident drinking and driving population.
RESULTS: Cross-sectional analyses showed that effects relating on-premise densities to alcohol-related crashes were moderated by highway trafficflow. Depending on levels of highway traffic flow, 10% greater densities were related to 0% to 150% greater rates of SVN crashes. Time-series cross-sectional analyses showed that changes in the population pool of drinking drivers and on-premise densities interacted to increase SVN crash rates.
CONCLUSIONS: A simple traffic-flow model can assess the effects of on-premise alcohol-outlet densities and of drinking-driver densities as they vary across communities to produce alcohol-related crashes. Analyses based on these models can usefully guide policy decisions on the sitting of on-premise alcohol outlets.