Effects of recent 0.08% legal blood alcohol limits on fatal crash involvement.

Boston University School of Public Health: Social and Behavioral Sciences Department, MA 02118, USA.
Injury Prevention (Impact Factor: 1.89). 07/2000; 6(2):109-14.
Source: PubMed


This study assessed whether states that lowered legal blood alcohol limits from 0.10% to 0.08% in 1993 and 1994 experienced post-law reductions in alcohol related fatal crashes.
Six states that adopted 0.08% as the legal blood alcohol limit in 1993 and 1994 were paired with six nearby states that retained a 0.10% legal standard. Within each pair, comparisons were made for the maximum equal available number of pre-law and post-law years.
States adopting 0.08% laws experienced a 6% greater post-law decline in the proportion of drivers in fatal crashes with blood alcohol levels at 0.10% or higher and a 5% greater decline in the proportion of fatal crashes that were alcohol related at 0.10% or higher.
If all states adopted the 0.08% legal blood alcohol level, 400-500 fewer traffic fatalities would occur annually.


Available from: Michael R Winter, Mar 12, 2014
  • Source
    • "All 50 states and the District of Columbia have now adopted 0.08 BAC per se laws. Though Foss, Stewart, and Reinfurt (2001) reported no reduction in fatalities after North Carolina lowered its legal limit to 0.08 BAC, numerous other studies did find that lowering the BAC reduces alcohol-related fatalities (Apsler et al., 1999; Hingson, Heeren, and Winter, 2000). Several studies have since found an association between 0.10 and 0.08 BAC per se laws and a reduction in motor vehicle fatalities (such as Dee, 2001; Eisenberg, 2003; Villaveces et al., 2003; Voas, Tippetts, and Fell, 2000). "
    [Show abstract] [Hide abstract]
    ABSTRACT: "In this study, we consider the effects of state alcohol policies on motor vehicle fatalities for children. While numerous studies have considered the effects of such policies on motor vehicle fatalities for the overall population, for teens, and for the elderly, their effects on fatalities among children in particular have not previously been studied. We use state-level cross-sectional time series data for 1982-2002. The dependent variable of interest is fatalities among child motor vehicle occupants (CMVO). Separate models are estimated for 0- to 4-yr-olds, 5- to 9-yr-olds, and 10- to 15-yr-olds, as well as for fatalities occurring during the day versus the night. We find that number of fatalities among CMVO is strongly correlated to alcohol use measured at the state level and that administrative license revocation policies and higher beer tax rates appear to consistently reduce such fatalities. For two of the three age groups, beer tax rates appear to reduce fatalities during the night rather than the day. However, zero tolerance and blood alcohol concentration limit laws do not seem to have any statistically significant effects on fatalities." ("JEL" I18, J13) Copyright (c) 2009 Western Economic Association International.
    Contemporary Economic Policy 07/2010; 28(3):392-405. DOI:10.1111/j.1465-7287.2009.00142.x · 0.60 Impact Factor
  • Source
    • "It has been one of the most commonly used statistical procedures to evaluate traffic safety interventions or other policies that can affect road safety (e.g. Abdel- Aty and Abdelwahab, 2004; Blose and Holder, 1987; Elder et al., 2004; Elder et al., 2002; Gruenewald and Ponicki, 1995; Hagge and Romanowicz, 1996; Hingson et al., 2000; Holder and Wagenaar, 1994; Holder et al., 2000; Langley et al., 1996; Mayhew et al., 2001; Murray et al., 1993; Nathens et al., 2000; Vernon et al., 2004; Voas et al., 1997). Indeed, various applied researchers have written specifically on the value of using time series analyses in intervention or evaluation research (Biglan et al., 2000; Gruenewald, 1997; Rehm and Gmel, 2001) Often applied researchers are under pressure to evaluate the impact of a traffic safety intervention soon after its implementation. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The evaluation of traffic safety interventions or other policies that can affect road safety often requires the collection of administrative time series data, such as monthly motor vehicle collision data that may be difficult and/or expensive to collect. Furthermore, since policy decisions may be based on the results found from the intervention analysis of the policy, it is important to ensure that the statistical tests have enough power, that is, that we have collected enough time series data both before and after the intervention so that a meaningful change in the series will likely be detected. In this short paper, we present a simple methodology for doing this. It is expected that the methodology presented will be useful for sample size determination in a wide variety of traffic safety intervention analysis applications. Our method is illustrated with a proposed traffic safety study that was funded by NIH.
    Accident Analysis & Prevention 06/2008; 40(3):1244-8. DOI:10.1016/j.aap.2007.10.007 · 1.87 Impact Factor
  • Source
    • "Mixed results Hingson et al. (1996) Five States (USA) Reduction in alcohol involvement Foss et al. (1998) State of North Carolin (USA) No clear effects Apsler et al. (1999) 11 States (USA) Significant reduction in alcohol involvement only in two states Hingson et al. (2000) Six States (USA) 6% decline in alcohol-related fatal crashes Voas et al. (2000) "
    [Show abstract] [Hide abstract]
    ABSTRACT: Road safety is of increasing concern in developed countries because of the significant number of deaths and large economic losses. One tool commonly used by governments to deal with road accidents is the enactment of stricter policies and regulations. Drunk driving is one of the leading concerns in this field and several European countries have decided to lower their illegal Blood Alcohol Content levels to 0.5 mg/ml over the last decade. This study uses European panel-based data (CARE) for the period 1991–2003 for the first time to evaluate the effectiveness of this transition by applying the differences-in-differences method in a fixed effects estimation that allows for any pattern of correlation (Cluster-Robust). The results show positive policy impacts, particularly on certain groups of victims, such as young males in urban zones. However, there are reasons to expect a short lag in that effectiveness. © 2008 by the Association for Public Policy Analysis and Management.
    Journal of Policy Analysis and Management 12/2007; 27(1):20 - 39. DOI:10.1002/pam.20305 · 0.93 Impact Factor
Show more