Evaluating 24/7 Sobriety Program participant reoffense risk |
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| Institution: | 1. Department of Transportation and Logistics, North Dakota State University, Fargo, ND, United States;2. Department of Mathematics, North Dakota State University, Fargo, ND, United States;3. Department of Statistics, North Dakota State University, Fargo, ND, United States;1. Department of Civil Engineering, California State Polytechnic University, Pomona, CA 91768, United States;2. Traffic Safety Investigations Branch, Department of Transportation California, United States;3. Division of Research, Innovation and System Information, Department of Transportation California, United States;4. Department of Logistics Engineering, Logistics and Traffic College, Central South University of Forestry and Technology, Hunan 410004, China;1. Department of Clinical Psychology, Louisiana State University, Baton Rouge, LA, United States;2. Minneapolis Veterans Affairs Health Care System, Minneapolis, MN, United States;3. Oregon Center for Aging & Technology, Portland, OR, United States;4. Department of Rehabilitation Medicine, University of Minnesota, MN, United States;5. Department of Psychiatry, University of Minnesota, Minneapolis, MN, United States;1. Department of Physical Education, Sports and Recreation, Universidad de La Frontera, Av Francisco Salazar 01145, 4780000, Chile;2. Queensland University of Technology (QUT), School of Public Health and Social Work, Victoria Park Road, Brisbane, 4059, Australia;3. Queensland University of Technology (QUT), Institute of Health and Biomedical Innovation, 60 Musk Ave., Kelvin Grove, QLD, 4059, Australia;4. UFRO Activate Research Group, Universidad de La Frontera, Av Francisco Salazar 01145, Temuco, 4780000, Chile;1. Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL 35487, United States;2. Alabama Transportation Institute, The University of Alabama, Tuscaloosa, AL 35487, United States;1. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia;2. Department of Computer Science, COMSATS Institute of Information Technology, Quaid Avenue, Wah Cantt, Pakistan;3. Department of Electrical and Computer Engineering, North Dakota State University, Fargo, ND 58108-6050, USA |
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| Abstract: | Objective: Our study investigated risk factors in survival among a subpopulation of drivers in North Dakota’s 24/7 Sobriety Program. Participants mandated for a second driving-under-the-influence of alcohol (DUI) arrest were studied for a three-year interval that commenced with the start date for a 360-day enrollment. Method: A Stratified Cox regression model was developed to compute the hazard ratios for survival. A subsequent DUI-related offense as event of interest. Relation to the explanatory variable array that could be construed from administrative records were investigated. Results: Older drivers were 6.31 times more likely to reoffend than the younger driver cohort of 18–35-years. The survival curve slope showed the fastest decline in the 361-day to 730-day interval. Neither gender nor residence region was a significant predictor in DUI reoffense over the three-year monitoring interval. Preliminary work suggests reoffense was more likely if an individual had program history prior to this court mandated 360-day term in the 24/7 Sobriety Program for a second DUI. The program experience finding was unexpected but could not be studied in greater detail due to data and resource limitations. Conclusions: Administrative records access created a novel opportunity to explore an evolving impaired driving prevention strategy that has shown early promise. Individual driver survival in and after the 24/7 Sobriety Program was studied for three-years. Findings show age, post-program time interval, and possibly program history as areas to explore to improve survival rates. Driver DUI offense were most common shortly after program completion. Although limited to a single state, findings increase knowledge for refining strategies designed to impact driver subpopulations at higher risk for reoffense. |
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| Keywords: | Survival analysis Alcohol-impaired driving Stratified Cox regression model |
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