Using Rural–Urban Continuum Codes (RUCCS) to Examine Alcohol-Related Motor Vehicle Crash Injury and Enforcement in New York State

Joyce C. Pressley, Leah M. Hines, Michael J. Bauer, Shin Ah Oh, Joshua R. Kuhl, Chang Liu, Bin Cheng and Matthew F. Garnett
Additional contact information
Joyce C. Pressley: Departments of Epidemiology and Health Policy and Management and Center for Injury Epidemiology and Prevention at Columbia, Columbia University, New York, NY 10032, USA
Leah M. Hines: Bureau of Occupational Health and Injury Prevention, New York State Department of Health, Albany, NY 12237, USA
Michael J. Bauer: Bureau of Occupational Health and Injury Prevention, New York State Department of Health, Albany, NY 12237, USA
Shin Ah Oh: Department of Epidemiology, Columbia University, New York, NY 10032, USA
Joshua R. Kuhl: Department of Epidemiology, Columbia University, New York, NY 10032, USA
Chang Liu: Department of Epidemiology, Columbia University, New York, NY 10032, USA
Bin Cheng: Department of Biostatistics, Columbia University, New York, NY 10032, USA
Matthew F. Garnett: Bureau of Occupational Health and Injury Prevention, New York State Department of Health, Albany, NY 12237, USA

IJERPH, 2019, vol. 16, issue 8, 1-17

Abstract: Rural areas of New York State (NYS) have higher rates of alcohol-related motor vehicle (MV) crash injury than metropolitan areas. While alcohol-related injury has declined across the three geographic regions of NYS, disparities persist with rural areas having smaller declines. Our study aim was to examine factors associated with alcohol-related MV crashes in Upstate and Long Island using multi-sourced county-level data that included the Crash Outcome Data Evaluation System (CODES) with emergency department visits and hospitalizations, traffic citations, demographic, economic, transportation, alcohol outlets, and Rural–Urban Continuum Codes (RUCCS). A cross-sectional study design employed zero-truncated negative binominal regression models to assess relative risks (RR) with 95% confidence interval (CI). Counties ( n = 57, 56,000 alcohol-related crashes over the 3 year study timeframe) were categorized by mean annual alcohol-related MV injuries per 100,000 population: low (24.7 ± 3.9), medium (33.9 ± 1.7) and high (46.1 ± 8.0) ( p < 0.0001). In multivariable analyses, alcohol-related MV injury was elevated for non-adjacent, non-metropolitan counties (RR 2.5, 95% CI: 1.6–3.9) with higher citations for impaired driving showing a small, but significant protective effect. Less metropolitan areas had higher alcohol-related MV injury with inconsistent alcohol-related enforcement measures. In summary, higher alcohol-related MV injury rates in non-metropolitan counties demonstrated a dose–response relationship with proximity to a metropolitan area. These findings suggest areas where intervention efforts might be targeted to lower alcohol-related MV injury.

Keywords: rural health; motor vehicle crash; injury; alcohol; traffic citations and enforcement (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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