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Introduction, acknowledgements, conflict of interest.

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Do Brief Alcohol Interventions Reduce Driving After Drinking Among College Students? A Two-step Meta-analysis of Individual Participant Data

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Eun-Young Mun, Xiaoyin Li, Shelby Lineberry, Zhengqi Tan, David Huh, Scott T Walters, Zhengyang Zhou, Mary E Larimer, in Collaboration with Project INTEGRATE Team, Do Brief Alcohol Interventions Reduce Driving After Drinking Among College Students? A Two-step Meta-analysis of Individual Participant Data, Alcohol and Alcoholism , Volume 57, Issue 1, January 2022, Pages 125–135, https://doi.org/10.1093/alcalc/agaa146

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College students who drink are at an increased risk of driving after drinking and alcohol-involved traffic accidents and deaths. Furthermore, the persistence of driving after drinking over time underscores a need for effective interventions to prevent future drunk driving in adulthood. The present study examined whether brief alcohol interventions (BAIs) for college students reduce driving after drinking.

A two-step meta-analysis of individual participant data (IPD) was conducted using a combined sample of 6801 college students from 15 randomized controlled trials (38% male, 72% White and 58% first-year students). BAIs included individually delivered Motivational Interviewing with Personalized Feedback (MI + PF), Group Motivational Interviewing (GMI), and stand-alone Personalized Feedback (PF) interventions. Two outcome variables, driving after two+/three+ drinks and driving after four+/five+ drinks, were checked, harmonized and analyzed separately for each study and then combined for meta-analysis and meta-regression analysis.

BAIs lowered the risk of driving after four+/five+ drinks (19% difference in the odds of driving after drinking favoring BAIs vs. control), but not the risk of driving after two+/three+ drinks (9% difference). Subsequent subgroup analysis indicated that the MI + PF intervention was comparatively better than PF or GMI.

BAIs provide a harm reduction approach to college drinking. Hence, it is encouraging that BAIs reduce the risk of driving after heavy drinking among college students. However, there may be opportunities to enhance the intervention content and timing to be more relevant for driving after drinking and improve the outcome assessment and reporting to demonstrate its effect.

Driving after drinking is a serious public health concern in the USA. Although the prevalence of overall alcohol-involved traffic fatalities ( National Center for Statistics and Analysis, 2019 ), young adult alcohol-involved traffic fatalities and binge drinking have decreased ( Hingson et al. , 2017 ; Schulenberg et al. , 2019 ) in the last three decades, the rates of driving after drinking, especially among young adults remain concerning and in need of further improvement. The consequences of driving after drinking are profound, with high individual and societal costs. Among young adults, alcohol plays a disproportionate role in alcohol-related fatalities. In 2014, 53% of traffic deaths among 18- to 24-year-olds involved alcohol compared with 33% in all other age groups ( Hingson et al. , 2017 ).

In 2014, an estimated 12% of the college students ages 18–20 and 22% of students ages 21–24 in the USA had driven under the influence of alcohol compared with 9% and 18%, respectively, of their non-college, same-age peers ( Hingson et al. , 2017 ). A recent 7-year longitudinal study of incoming college students suggests that driving after drinking may be even more prevalent ( Caldeira et al. , 2017 ). After excluding students without access to a car, Caldeira and colleagues found that the prevalence of driving after drinking peaked at age 22 (66.9%) without any clear declines through age 24. Furthermore, driving after drinking persisted from year to year (83.9–89.1%) for 7 years, indicating that 8 or 9 out of 10 young adults who drive after drinking in a given year do so again in the subsequent years ( Caldeira et al. , 2017 ). Not surprisingly, a prior incident of alcohol-impaired driving is a strong predictor of future alcohol-impaired driving (see also LaBrie et al., 2012 ; Teeters et al. , 2015 ). Similarly, drivers involved in fatal traffic accidents with a blood alcohol concentration (BAC) of 0.08% or higher were 4.5 times more likely to have prior convictions for driving while impaired ( National Center for Statistics and Analysis, 2018 ).

Despite steady progress since the 1980s in reducing alcohol-related harm among young adults, work remains to prevent alcohol-involved driving among young adults and to sever its link to future drunk driving. Compared to alcohol-related problems that represent an actual consequence of drinking (e.g. a missed day of school), driving after drinking is a high-risk behavior that is connected with the potential for harm (e.g. traffic accidents that may or may not happen). Interestingly, Nguyen et al. (2013) reported that college students who reported recent driving after drinking were less likely to plan to limit their drinking in the future compared with students who did not report any recent driving after drinking. This suggests that it is important to prevent driving after drinking, not only because of the immediate safety risks but also because it can ‘embolden’ students to future risky behavior.

In the context of preventive interventions, it is critical to evaluate the effectiveness of existing alcohol interventions in lowering the risk of alcohol-involved driving among young adults. This knowledge would be the first step toward improving existing intervention strategies and developing alternative approaches to reducing driving after drinking. Unfortunately, although driving after drinking is an important and preventable alcohol-related problem and a frequent target of alcohol interventions, few studies have reported driving after drinking as a specific and separate outcome.

Large-scale evidence on the effectiveness of brief alcohol interventions for driving after drinking

There are two meta-analysis studies of driving after drinking using aggregate data (AD; i.e. study-level summary data, such as means, standard deviations, odds ratios [ORs], which are aggregated at the treatment arm level or at the study level) and one using individual participant data (IPD; i.e. original participant-level response), which analyzed driving after drinking as a separate clinical outcome of brief alcohol interventions (BAIs) for young adults ( Steinka-Fry et al. , 2015 ; Teeters et al. , 2015 ; Foxcroft et al. , 2016 ). Steinka-Fry et al. (2015 ) identified 12 BAI studies for a combined sample of 5664 young adults (mean age = 17, with an age range from 13.5 to 20.5 years). They found a statistically significant, though modest standardized effect size, |$\hat{g}$| = 0.15 (95% confidence interval [CI] = 0.08, 0.21), favoring the BAIs compared to controls. However, they also discussed the evidence of potential publication bias from selective underreporting of null or negative findings from the existing trials. Therefore, the overall effect size from this meta-analysis may be an overestimate. Furthermore, the average age in this combined sample was 17, an age that triggers graduated driver licensing programs (e.g. supervised driving or nighttime driving restriction) for novice and beginning drivers in many states in the USA ( Williams et al. , 2016 ). Therefore, the reported intervention effect should be interpreted cautiously with these points in mind.

Foxcroft et al. (2016) identified six interventions that utilized Motivational Interviewing (MI) for young adults up to age 25 and analyzed outcomes at less than 4 months follow-up (four comparisons from three studies) and at 4 or more months follow-up (four comparisons from four studies). The standardized mean difference estimates on driving after drinking were −0.22 ( P  < 0.05), favoring the interventions compared to controls, at follow-up that occurred less than 4 months from intervention and −0.13 ( P  > 0.05; no evidence of any group difference) at 4 or more months follow-up. Due to the small sample of studies (i.e. six) and the small number of comparisons (i.e. four each) in this meta-analysis, combined with the limited evidence previously noted in general, it is difficult to determine whether MI interventions are effective for lowering the risk of driving after drinking.

Teeters et al. (2015) examined IPD from three separate trials ( Murphy et al. , 2010 ; Borsari et al. , 2012 ; Martens et al. , 2013 ), where driving after drinking was not previously reported but was measured as a clinical outcome in all three trials. Four brief motivational intervention (BMI) groups in three samples of college student drinkers were separately analyzed. The study found that BMIs focusing on clarifying misconceptions around drinking norms using an MI style ( Miller and Rollnick, 2013 ) were effective in reducing driving after drinking at 6 or 9 months post-intervention. However, a single-component BMI focusing on the use of protective behavioral strategies, which was facilitated by an MI therapist, was not effective in reducing driving after drinking, suggesting effect heterogeneity within BMIs. Despite providing a wealth of information on the association between BMIs and driving after drinking using the most granular data (i.e. IPD), this study was limited by small sample size at the study level (i.e. three); the use of a single-item outcome question from the Brief Young Adult Alcohol Consequences Questionnaire ( Kahler et al. , 2005 ), which can be subjectively interpreted (‘I have driven a car when I knew I had too much to drink to drive safely’); and a separate analysis of three data sets without quantitatively combining data.

In sum, the available evidence cautiously suggests an intervention effect on driving after drinking for young adults. However, due to the noted limitations, it remains an open question as to the extent BAIs are effective and whether there are variables that moderate the intervention effect.

BAIs and IPD meta-analysis

Given that driving after drinking is an important clinical endpoint of alcohol interventions for college students, the current study examined whether BAIs lower the risk of driving after drinking using IPD in a two-step IPD meta-analysis. In the current study, we used the term BAI to include BMIs (e.g. the Brief Alcohol Screening and Intervention for College Students [BASICS; Dimeff, 1999 ]), which are based on a harm reduction approach ( Marlatt and Witkiewitz, 2002 ) and typically facilitated by using MI principles to elicit motivation and commitment to change ( Miller and Rollnick, 2013 ). In addition, stand-alone Personalized Feedback (PF) interventions without any in-person facilitation or group-based motivational interviewing interventions are inclusively referred to as BAIs in the current study. BAIs are generally efficacious for reducing alcohol consumption and alcohol-related problems for college students (for reviews, see Carey et al. , 2007 , 2016 ; Cronce and Larimer, 2011 ). A more fine-grained investigation using IPD revealed that the overall effect of BAI on alcohol consumption and alcohol-related problems might be small ( Huh et al. , 2015 , 2019 ), with evidence that the combination of in-person MI and PF (MI + PF) effectively reduces alcohol-related problems ( Huh et al. , 2015 ; Jiao et al. , 2020 ). However, whether BAIs have beneficial effects on reducing driving after drinking has not been studied.

The current study aims to overcome the limitations of existing evidence (e.g. small samples at the study level, limitations in measurement and sample characteristics and potential underreporting of null or negative effects) using IPD from Project INTEGRATE ( Mun et al. , 2015b ) in a two-step IPD meta-analysis, where AD from individual studies are obtained by analyzing the corresponding IPD in the first step and then combined in the second step. Two-step IPD meta-analysis is currently the prevailing approach to IPD meta-analysis ( Simmonds et al. , 2015 ).

Meta-analysis has traditionally involved the analysis of summary data (i.e. AD) extracted from available published and unpublished reports. However, the analysis of IPD obtained from the original studies has emerged as the gold standard approach to meta-analysis ( Sutton and Higgins, 2008 ). When the goal is to characterize average treatment effects, AD meta-analysis and IPD meta-analysis often produce comparable results (see Sutton and Higgins, 2008 , for review and discussion). However, key advantages of IPD over AD in meta-analysis include the ability to check the data more carefully, ensure that the same analysis is conducted across all studies, evaluate subgroup differences in the magnitude of treatment effect, distinguish between-study vs. within-study effect modifiers (i.e. moderators) for ecologically valid inference and test moderators with greater power, compared to AD meta-analysis ( Cooper and Patall, 2009 ; Riley et al. , 2010 ). More importantly, with IPD, it is possible to conduct a meta-analysis of previously unreported outcomes. In the current study, we checked data and ensured the same analysis for the same interpretation across all studies in a novel meta-analysis of unreported alcohol-impaired driving outcomes in a two-step IPD meta-analysis. Given that driving after drinking is one of the potential consequences of heavy drinking, we hypothesized that BAIs would have a protective treatment effect and that MI + PF would be comparatively better than PF.

Participants

The data come from Project INTEGRATE ( Mun et al. , 2015b ), an ongoing large-scale synthesis study aimed at examining the comparative effectiveness of BAIs for reducing alcohol misuse among college students by utilizing IPD. All BAIs were delivered individually in person, in group, via mail or computer/online. All BAIs were considered brief but differed in the content topics covered and levels of personalization ( Ray et al. , 2014 ). Of the 24 studies with available IPD ( N  = 12,630 participants) that we obtained from the original investigators, 15 studies met the following inclusion criteria: (a) at least two-arm randomized trials with a control or comparison group and (b) available outcome (i.e. driving after drinking) measures at baseline and a follow-up within 12 months post-intervention (see Fig. 1 and Table 1 ). At baseline, IPD from 9485 participants from 15 studies were available. Of those, 2684 participants did not have outcomes at a first follow-up within 12 months post-intervention, resulting in a sample of 6801 participants (38% male, 72% White and 58% first-year student) across the 15 studies.

PRISMA IPD flow diagram. IPD come from Project INTEGRATE ( Mun et al. , 2015b ), and data flow at the stages of identification, screening and eligibility is not applicable (unknown).

Description of the IPD at Baseline ( N  = 9485; 35 randomized groups and 15 studies)

Note . Study 7 is a single study but has two subsamples. 7.1 = Mandated student sample and 7.2 = Voluntary student sample. Control groups were waitlisted (Study 2), assessment-only controls (Studies 7, 8a, 8b, 8c, 9, 10.1, 11, 15, 16 and 19–21) or comparison groups that received general alcohol education (Studies 3 and 4). Control students in Studies 15, 16 and 20 received a packet or single-page information sheet containing information about alcohol use. PF, stand-alone personalized feedback intervention; MI + PF, in-person motivational interviewing intervention with personalized normative feedback profile; GMI, group motivational interviewing intervention.

Interventions and controls

Of the 15 studies that met the inclusion criteria, 13 studies were two-arm trials, two studies were multi-arm trials and one study had two subsamples, resulting in a total of 35 treatment arm groups: Five individually delivered Motivational Interviewing with Personalized Feedback (MI + PF) interventions, six Group Motivational Interviewing (GMI) interventions, eight stand-alone PF interventions and 16 Control groups (see Table 1 ).

Control groups were waitlist controls (Study 2), assessment-only controls (Studies 7, 8a–8c, 9, 10, 11, 15, 16 and 19–21) or comparison groups that received general alcohol education (Studies 3 and 4). Control group students in studies 15, 16 and 20 received a packet or single-page information sheet containing information about alcohol use. Therefore, the control/comparison groups were mostly assessment-only controls or were exposed to minimal intervention content. More details on these intervention groups can be found in previous articles ( Ray et al. , 2014 ; Mun et al. , 2015b ; Mun and Ray, 2018 ).

All but one study (Study 19) provided general information on BAC and intoxication as well as content on driving while intoxicated (DWI). Except for Study 2, which tailored DWI content to participants, all other studies provided general DWI content to participants. Studies 3, 4, 15, 16 and 20 also provided general information on BAC for control students, and Studies 3, 4, 15 and 16 provided general information on DWI to control students.

Driving after drinking outcomes

Most of the driving after drinking outcome variables included in the current study (see Table 2 ) come from two items added to the Rutgers Alcohol Problems Index ( White and Labouvie, 1989 ). Studies 11 and 21 used a modified question, Study 7 had open-ended frequency questions about driving after drinking and Study 3 included an item from the Young Adult Driving Questionnaire ( Donovan, 1993 ). These items varied slightly in terms of their wording (e.g. driving after two+, three+, four+, five+ drinks), referent time frame, response options and follow-up assessment timing. We harmonized them into two drinking and driving (DD) outcomes for a dichotomous response (1 = yes; 0 = no): Driving after more than two (two+) or three (three+) drinks (15 studies, 19 comparisons), and driving after more than four (four+) or five (five+) drinks (12 studies; 15 comparisons). Across all studies with the two outcomes, there was a correlation of 0.92.

The measure of driving after drinking by study

Notes. The referent time period for DD outcome was 1 month for Studies 7, 11, 15, 16, 19 and 20; 2 months for Study 2; 3 months for Studies 3, 8a, 8b, 8c, 9, 21; and 6 months for Studies 4 and 10.1. BL, baseline; FU, follow-up; DD1, driving after two+/three+ drinks; DD2 = driving after four+/five+ drinks.

Analysis plan

The current study used a two-step IPD meta-analysis. We utilized a random-effects meta-analysis model to obtain pooled effect sizes for both outcomes, which were analyzed separately. A random-effect meta-analysis model is based on more reasonable assumptions compared to a fixed-effect meta-analysis model, such as allowing heterogeneity in effect sizes across individual studies. For study |$i$|⁠ , in addition to within-study variability |${s}_i^2$|⁠ , a random-effects model assumes between-study effect size variation surrounding the underlying true common effect size |$\theta$|⁠ , which can be expressed as the variance |${\tau}^2$|⁠ . Therefore, the observed effect size |${y}_i$| is assumed to be normally distributed with the corresponding study-specific true effects |${\theta}_i$| and sampling variance |${s}_i^2$| for study |$i$|⁠ . The true effects |${\theta}_i$| are, in turn, normally distributed with the average, underlying true effect |$\theta$| and variance |${\tau}^2$|⁠ . These relationships can be expressed more formally as follows:

|$\mathrm{Level}\ 1:{y}_i\;{\Big|\;{\theta}_i},{s}_i^2\sim N\Big({\theta}_i,{s}_i^2\Big)$|

|$\mathrm{Level}\ 2:{\theta}_i\;\Big|\;\theta, {\tau}^2\sim N\Big(\theta, {\tau}^2\Big)\Big..$|

We used the OR as an effect size, one of the standard effect size measures for binary outcome variables, and displayed the overall effect size on the log OR (LOR) scale. The OR is the ratio of the odds for the intervention group to the odds for the control group having the outcome of interest. In the present study, an OR less than 1 indicates a favorable outcome for the intervention (i.e. less likely to drive after drinking for BAI vs. control), whereas an OR greater than 1 indicates a favorable outcome for the control group. All data preparations were conducted using SAS 9.4 (SAS Institute Inc., Cary, NC) and meta-analyses were conducted using the package ‘metafor’ version 2.4 ( Viechtbauer, 2010 ) for R version 4.0.1 ( R Core Development Team, 2020 ). All statistical tests used a two-sided significance level of 0.05. We also conducted subgroup meta-analysis as well as meta-regression to identify potential moderators. All data and computing code in R can be accessed in the online repository ( Mun et al. , 2021 ).

Descriptive analysis

Figure 2 shows the percentage of driving after drinking for intervention and control groups for each study at baseline and follow-up. Figure 2 graphically illustrates the between-study heterogeneity in the outcomes. The percentage of driving after two+/three+ drinks and driving after four+/five+ drinks ranged from 1.9 to 45.8% and from 0 to 30.9%, respectively, at follow-up. In addition, there appear to be different degrees of effectiveness in reducing driving after drinking across BAI groups and studies.

Percentage of driving after two+/three+ drinks (top) and driving after four+/five+ drinks (bottom) at baseline (B) and follow-up (F) per group by study.

Figure 3 displays contour-enhanced funnel plots for both outcomes. Funnel plots are used to detect any sign of publication bias. Figure 3 shows that effect sizes are symmetrically distributed without clear signs of ‘missing’ studies in specific regions of statistical significance or precision.

Contour-enhanced funnel plots of LORs: Driving after two+/three+ drinks (top) and after four+/five+ drinks (bottom). Filled circles indicate study-specific effect size estimates. When they are outside the triangle, they are statistically significant at P  ≤ 0.05. The outer triangle layer in gray shows the area where 0.05 <  P  ≤ 0.10, and the inner triangle in white indicates the region where 0.10 <  P  ≤ 1.00.

Overall effectiveness of BAI on driving after drinking

The top panel of Fig. 4 shows that the effectiveness of BAIs in lowering the risk of driving after two+/three+ drinks was not statistically significant, LOR = −0.10 across k  = 19 comparisons, z  = −1.54 (OR = 0.91; 95% CI = 0.80, 1.03), P  = 0.12. Most of the individual trial effect sizes (12 out of 19) were in the direction of favoring the BAIs. On average, the odds of driving after drinking was reduced by 9% for individuals after receiving BAI (vs. control), although the difference was not statistically significant.

Forest plots of driving after two+/three+ drinks (top) and driving after four+/five+ drinks (bottom). DD, reported driving after drinking; no DD, did not report any driving after drinking.

The bottom panel of Fig. 4 shows the outcome of driving after four+/five+ drinks, where BAIs had a statistically significant intervention effect compared to controls, LOR = −0.21 across k  = 15 comparisons, z  = −2.15 (OR = 0.81; 95% CI = 0.67, 0.98), P  = 0.03. Most of the individual effect sizes (11 out of 15) were in the direction of favoring the BAIs, on average, reducing the odds of driving after drinking by 19%, compared to their counterpart controls.

Study-level moderators of the BAI effect

Subsequent subgroup analysis indicated that MI + PF was statistically significant in lowering the risk of driving after four+/five+ drinks, LOR = −0.61 (OR = 0.54), k  = 3, z  = −3.03, P  < 0.01. The college students allocated to the MI + PF group reduced their risk by 46%, compared with their counterpart controls. Neither GMI nor PF statistically significantly lowered the risk of driving after four+/five+ drinks, LOR = −0.02 (OR = 0.98), k  = 6, z  = −0.09, P  = 0.92 and LOR = −0.15 (OR = 0.86), k  = 6, z  = −1.21, P  = 0.23, respectively. For the outcome of driving after two+/three+ drinks, none of the BAI subgroups showed a statistically significant effect, MI + PF: LOR = −0.13 (OR = 0.87), k  = 5, z  = −0.65, P  = 0.51; GMI: LOR = −0.01 (OR = 0.99), k  = 6, z  = −0.04, P  = 0.97; and PF: LOR = −0.09 (OR = 0.91), k  = 8, z  = −1.13, P  = 0.26.

We further probed whether the referent time window or whether the follow-up duration helped to explain the null finding for driving after two+/three+ drinks. Having a longer referent time frame may mean that students have more opportunities to drive after drinking and, consequently, more opportunities for the BAIs to have a positive impact. Meta-regression analysis results showed a trend toward a larger effect with a longer referent time frame (i.e. in the past 3 months vs. the past month) for driving after two+/three+ drinks, LOR = −0.08 (OR = 0.92), k  = 19, z  = −1.85, P  = 0.06 (see Fig. 5 ). With each month, the odds of driving after drinking for BAIs, compared to controls, decreased an average of 8% points. Similarly, with the follow-up period, there was a nonsignificant effect, LOR = −0.02 (OR = 0.98), k  = 19, z  = −1.55, P  = 0.12.

A trend toward better intervention effects (shown in a solid line) on driving after two+/three+ drinks for studies with a longer the referent time period. A dotted line shows a reference line where OR = 1 (i.e. null effect). The size of the circles corresponds to the weighted study sample size. Dashed curved lines indicate the 95% CI.

BAIs are effective for reducing the risk of driving after four+/five+ drinks

We found that BAIs statistically significantly lowered the risk of driving after drinking four+/five+ drinks for college students, but not the risk of driving after two+/three+ drinks. In addition, subsequent subgroup analysis indicated that MI + PF mostly carried that significant effect. Given that BAIs are motivated by a harm reduction approach, it is encouraging that BAIs reduce the likelihood of driving after drinking heavily.

The 9% reduction in odds for driving after two+/three+ drinks, though not statistically significant, and 19% reduction for four+/five+ drinks, which would approximately correspond to −0.06 and −0.12, respectively, in the standardized mean difference, should be interpreted in the context that many studies included in this meta-analysis provided BAC and DWI information to both intervention and control students. If control students did not get information on BAC or DWI, the observed effect might have been greater. In addition, most interventions did not specifically tailor their BAC or DWI information to individual students. Although the effect of personalization on clinical endpoints may be complex ( Ray et al. , 2014 ), the findings from the current study suggest a need to revisit how intervention can be improved for driving after drinking.

Note that the reported effect in the current study appears modest compared with the standardized mean difference effect size of 0.15 in Steinka-Fry et al. (2015) . Given that Steinka-Fry et al . reported evidence of potential publication bias, the current finding, mostly from unreported outcomes, may be closer to the true population-level estimate. We translated the estimated ORs to standardized mean difference effect sizes to provide a comparative context because existing meta-analysis studies have reported effect sizes in the unit of a standard normal variable. However, ORs have a meaningful interpretation for a binary outcome ( von Eye and Mun, 2003 ) and are intuitively easier to understand for policy makers and the public ( Mun et al. , 2010 ).

How to improve the effect of BAIs?

One of the difficulties in delivering interventions to college students for driving after drinking is that first-year students often live on-campus without access to a car and opportunities to drive. This situational factor may make interventions for driving after drinking less relevant for younger students. More than half of the sample in the current study was first-year students (58%), and we nonetheless found a significant effect for driving after four+/five+ drinks. A targeted intervention for driving after drinking, if delivered to students ages 21 or older, may yield greater benefits than what was reported in the current study because the prevalence of driving after drinking may plateau later (e.g. age 22; Caldeira et al. , 2017 ).

A recent study from the Monitoring the Future also suggests that the mean age of peak binge drinking has steadily increased from ages 19.7 and 20.7 among women and men, respectively, to ages 21.6 and 23.0 in a more recent cohort of high school graduates (in 1996–2004), compared with older cohorts ( Patrick et al. , 2019 ). Given that binge drinking accounts for 85% of all alcohol-impaired driving episodes by those who report binge drinking ( Jewett et al. , 2015 ), there is a need to develop alcohol interventions for this slightly older college student population to reduce high-risk behaviors such as driving and drinking. Instead of maturing out, young adults in their early- to mid-20s sometimes experience a developmental transition period marked by more frequent risky behaviors, including DD. Social norm information about peer acceptance of driving after drinking as well as information about alcohol-related impairment in executive functions may be helpful.

We found that MI + PF had the strongest intervention effect on driving after drinking four+/five+ drinks. This result is consistent with earlier reports that identified MI + PF as effective for reducing alcohol-related problems through 12 months post-intervention ( Huh et al. , 2015 ; Jiao et al. , 2020 ). In contrast, the beneficial effects of BAIs on alcohol consumption may be comparatively short-lived. Reductions in alcohol consumption (such as frequency or quantity) are important goals by themselves but may not be necessary conditions for reducing alcohol-related harm. For example, Teeters et al. (2015 ) found that reductions in alcohol consumption did not predict driving after drinking at follow-up. Further investigations can provide an insight into these causal mechanisms.

Methodologically, we note a few observations. First, it may be desirable to establish targeted clinical endpoints and outcome measures for future BAIs. In the absence of consensus outcomes, many different measures are assessed in individual trials, and many important outcomes may not be fully reported in practice. For example, of the possible outcomes from the studies included in Project INTEGRATE, only half of the outcomes assessed were actually reported ( Li et al. , 2019 ). From a meta-analysis perspective, it is imperative that trials report all outcomes regardless of whether they have null or negative findings. The fact that all studies but one (Study 7) included in this meta-analysis have not been previously reported in other published meta-analysis reports emphasizes the need to report all outcomes in the original trials for better discoverability. Second, when designing an intervention targeting driving after drinking, it may be advantageous to use a longer referent time frame (e.g. in the past 3 months rather than in the past month) and a longer follow-up period (e.g. 6–12 months rather than 3 months) to appropriately capture the effect of this low-base rate behavior.

Limitations and implications

The current study addressed some of the limitations of the existing studies—potential publication bias, limitations in the sample and measure, small N at the study level and lack of quantitative synthesis. Nonetheless, the current study is not without limitations. First, this study included an IPD sample that was not systematically searched and obtained. Although the combined sample is a reasonably good representation of the existing BAIs between 1990 and 2010 ( Mun et al. , 2015b ), how representative this IPD sample is, relative to a sample that was systematically searched and obtained, remains a question. Relatedly, the current study reports data from trials of comparable recency with respect to existing meta-analysis studies. However, there have been promising BAIs that utilize smartphone technology, supplemental components or stakeholder buy-in for greater effects in the past decade. Clarifying the comparative effectiveness of these divergent intervention approaches, compared with the earlier generation of BMIs, remains an important research question.

Second, we had several comparisons that were nested within studies. Although it may not be ideal to analyze more than one effect size from the same study as if effect sizes were independent, this practice is fairly common. Also, given that most of the trials were two-arm trials, its effect on the inference may be limited. Third, we analyzed two outcomes separately, which may be seen as a limitation. The two outcomes within studies were highly correlated ( r  = 0.92). Although within-study correlations typically improve estimation in a multivariate meta-analysis ( Jackson et al. , 2011 ), this created an estimation challenge due to non-convergence in our study. We deemed it more important to provide results at two different tiers of drinking for substantive interpretation. Finally, we note that between-study heterogeneity in effect sizes was not substantial, at least based on the relevant statistics (see Fig. 4 ). Nonetheless, study-level variations existed, which can be examined in future IPD meta-analysis studies.

The current study, to our best knowledge, is the largest-scale meta-analysis on driving after drinking among young adults (15 studies, 34 comparisons, N  = 6801). In addition, this is the first IPD meta-analysis on this critical outcome. Using IPD, we checked and ensured data accuracy, ensured that AD from different studies would have the same interpretation and appropriately synthesized them across studies in a meta-analysis. Since the 1980s, meta-analysis applications have proliferated ( Cheung, 2015 ; Ioannidis, 2016 ). With the proliferation, the concern about low-quality or redundant meta-analysis reviews has also surfaced ( Ioannidis, 2016 ). The field of BAIs has not been an exception as we have previously discussed ( Mun et al. , 2015a ). The use of IPD, which offers the most fine-grained information, may improve the body of evidence in the field, which is increasingly feasible with greater data sharing and advances in computing methods.

We thank Nickeisha Clarke, Yang Jiao, Su-Young Kim and Anne E. Ray, for their earlier work on coding and harmonizing interventions and outcomes, and Helene R. White for her valuable conceptual and methodological contributions in the early years of Project INTEGRATE.

The project described was supported by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) (Award Number R01 AA019511). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIAAA or the National Institutes of Health.

None declared.

Project INTEGRATE Team: The Project INTEGRATE team consists of the following contributors in alphabetical order: John S. Baer, Department of Psychology, The University of Washington, and Veterans’ Affairs Puget Sound Health Care System; Nancy P. Barnett, Center for Alcohol and Addiction Studies, Brown University; M. Dolores Cimini, University Counseling Center, The University at Albany, State University of New York; William R. Corbin, Department of Psychology, Arizona State University; Kim Fromme, Department of Psychology, The University of Texas, Austin; Joseph W. LaBrie, Department of Psychology, Loyola Marymount University; Mary E. Larimer, Department of Psychiatry and Behavioral Sciences, The University of Washington; Matthew P. Martens, Department of Educational, School, and Counseling Psychology, The University of Missouri; James G. Murphy, Department of Psychology, The University of Memphis; Scott T. Walters, Department of Health Behavior and Health Systems, The University of North Texas Health Science Center; Helene R. White, Center of Alcohol and Substance Use Studies, Rutgers, The State University of New Jersey and the late Mark D. Wood, Department of Psychology, The University of Rhode Island.

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• harm reduction
• traffic accidents
• alcoholic beverages
• risk reduction
• driving under the influence
• alcohol intervention
• motivational interviewing
• outcome variable
• college students
• individual participant data

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• Open access
• Published: 12 March 2015

Driving under the influence of alcohol: frequency, reasons, perceived risk and punishment

• Francisco Alonso 1 ,
• Juan C Pastor 1 ,
• Luis Montoro 2 &
• Cristina Esteban 1  

Substance Abuse Treatment, Prevention, and Policy volume  10 , Article number:  11 ( 2015 ) Cite this article

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The aim of this study was to gain information useful to improve traffic safety, concerning the following aspects for DUI (Driving Under the Influence): frequency, reasons, perceived risk, drivers' knowledge of the related penalties, perceived likelihood of being punished, drivers’ perception of the harshness of punitive measures and drivers’ perception of the probability of behavioral change after punishment for DUI.

A sample of 1100 Spanish drivers, 678 men and 422 women aged from 14 to 65 years old, took part in a telephone survey using a questionnaire to gather sociodemographic and psychosocial information about drivers, as well as information on enforcement, clustered in five related categories: “Knowledge and perception of traffic norms”; “Opinions on sanctions”; “Opinions on policing”; “Opinions on laws” (in general and on traffic); and “Assessment of the effectiveness of various punitive measures”.

Results showed around 60% of respondents believe that driving under the influence of alcohol is maximum risk behavior. Nevertheless, 90.2% of the sample said they never or almost never drove under the influence of alcohol. In this case, the main reasons were to avoid accidents (28.3%) as opposed to avoiding sanctions (10.4%). On the contrary, the remaining 9.7% acknowledged they had driven after consuming alcohol. It is noted that the main reasons for doing so were “not having another way to return home” (24.5%) and alcohol consumption being associated with meals (17.3%).

Another important finding is that the risk perception of traffic accident as a result of DUI is influenced by variables such as sex and age. With regard to the type of sanctions, 90% think that DUI is punishable by a fine, 96.4% that it may result in temporary or permanent suspension of driving license, and 70% that it can be punished with imprisonment.

Conclusions

Knowing how alcohol consumption impairs safe driving and skills, being aware of the associated risks, knowing the traffic regulations concerning DUI, and penalizing it strongly are not enough. Additional efforts are needed to better manage a problem with such important social and practical consequences.

In Europe, traffic accidents are one of the main causes of mortality in people between 15 and 29 years old, and driving under the influence of alcohol (DUI) is a major risk factor in most crashes [ 1 , 2 ].

In the year 2001 in Spain, 40,174 people were treated in public hospitals for traffic injuries. Some 28% of these injuries were serious or very serious and drinking was involved in a high percentage of cases. According to the Spanish Directorate General of Traffic (DGT), alcohol is involved in 30-50% of fatal accidents and in 15 to 35% of crashes causing serious injury, constituting a major risk factor in traffic accidents. This problem is especially important among young people and worsens on weekend nights [ 3 , 4 ].

In more recent years, several studies have shown that more than a third of adults and half of teenagers admit they have driven drunk. We also know that most of them were not detected. Generally, the rate of arrests for driving under the influence is very low and even those drivers who were arrested were mostly “first-time” offenders [ 5 ].

Some studies show that many young people lack information or knowledge about the legislation regulating consumption of alcohol for drivers, as well as the effects of this drug on the user [ 6 - 8 ].

There are also some widespread beliefs and misconceptions regarding the actions the driver can take in order to neutralize the effects of alcohol before driving (for instance drinking coffee, having a cold shower or breathing fresh air). As suggested by Becker’s model of health beliefs [ 9 , 10 ], preventive behavior is unlikely to occur unless the subject considers the action necessary, hence the importance of providing adequate information and disproving false beliefs.

Drivers are not usually aware of the risk they assume when they drive under the influence of alcohol, as they do not suffer a traffic accident every time they drink and drive. Hence they tend to think there is no danger in driving under the influence of alcohol, incurring the same risk behavior once and again.

But the reality is quite different. Alcohol causes very obvious alterations in behavior, as it affects almost all the physical skills we need for safe driving. It can interfere with attention, perceptual functioning and motor skills, as well as in decision making while driving.

Drinking impairs the ability to drive and increases the risk of causing an accident. The effects of alcohol consumption on driving-related functions are modulated by some factors, such as form of consumption (regular or infrequent), expectations about their consumption, expertise in driving and driver’s age. The increased risk of accident starts at a lower blood alcohol level when drivers are inexperienced or they are occasional drinkers, and begins at a higher blood alcohol level when these are more experienced drivers or regular drinkers [ 11 , 12 ].

The BAC represents the volume of alcohol in the blood and is measured in grams of alcohol per liter of blood (g / l) or its equivalent in exhaled air.

Any amount of alcohol in blood, however small, can impair driving, increasing the risk of accident. Therefore, the trend internationally is to lower the maximum rates allowed.

After drinking, the rate of alcohol in blood that a driver is showing can vary widely due to numerous modulating variables. Among them, some important factors are the speed of drinking, the type of alcohol (fermented drinks such as beer or wine, or distilled beverages like rum or whisky) or the fact of having previously ingested some food, as well as the age, sex or body weight. Ideally, if everyone drank alcohol responsibly and never drove after drinking many deaths would be avoided. Accurate information about how driving under the influence effects traffic safety would be a positive step towards this goal.

Study framework

Research on enforcement of traffic safety norms has a long tradition. In 1979, a classic work [ 13 ] showed that increasing enforcement and toughening sanctions can reduce accidents as an initial effect, although the number of accidents tends to normalize later.

Justice in traffic is needed insofar as many innocent people die on the roads unjustly. This is our starting point and our central principle. In order to prevent traffic accidents, a better understanding is needed of the driver’s knowledge, perceptions and actions concerning traffic regulations. Drivers have to be aware of how important rules are for safety. The present study comes from a broader body of research on traffic enforcement, designed to develop a more efficient sanctions system [ 5 , 14 ].

Our research used a questionnaire to gain sociodemographic and psychosocial information about drivers, as well as additional information on enforcement clustered in five related categories: “Knowledge and perception of traffic norms”; “Opinions on sanctions”; “Opinions on policing”; “Opinions on laws” (general ones and traffic laws in particular); and “Assessment of the effectiveness of various punitive measures”.

A number of additional factors were also explored, including: driving too fast or at an improper speed for the traffic conditions, not keeping a safe distance while driving, screaming or verbal abuse while driving, driving under the influence, smoking while driving, driving without a seat belt and driving without insurance. For a more complete review, see the original study [ 14 ].

The aim of this study was to gain useful information to improve traffic safety, concerning the following aspects:

Frequency of driving under the influence of alcohol (DUI).

Reasons for either driving or not driving under the influence (DUI).

Perceived risk of DUI.

Drivers’ knowledge of DUI-related penalties.

The perceived likelihood of being punished for DUI.

Drivers’ perception of the harshness of punitive measures for DUI.

Drivers’ knowledge of the penalties for DUI.

Drivers’ perception concerning the probability of behavior change after punishment for DUI.

Sociodemographic and psychosocial factors related with alcohol consumption and driving.

Participants

The sample consisted of 1100 Spanish drivers: 678 men (61.64%) and 422 women (38.36%), between 14 and 65 years of age. The initial sample size was proportional by quota to segments of Spanish population by gender and age. The number of participants represents a margin of error for the general data of ± 3 with a confidence interval of 95% in the worst case of p = q = 50%; with a significance level of 0.05.

Drivers completed a telephone survey. 1100 drivers answered interviews, and the response rate was 98.5%; as it was a survey on social issues, most people consented to collaborate.

Procedure and design

The survey was conducted by telephone. A telephone sample using random digit dialing was selected. Every phone call was screened to determine the number of drivers (aged 14 or older) in the household. The selection criteria were possession of any type of driving license for vehicles other than motorcycles and driving frequently. Interviewers systematically selected one valid driver per home. The survey was carried out using computer assisted telephone interview (CATI) in order to reduce interview length and minimize recording errors, ensuring the anonymity of the participants at all times and emphasizing the fact that the data would be used only for statistical and research purposes. The importance of answering all the questions truthfully was also stressed.

In this article, we present the data on driving under the influence of alcohol. The first question raised was: How often do you currently drive after drinking any alcoholic beverage? Possible responses were: Almost always, Often, Sometimes, Rarely or Never.

If they answered either Almost always, Often or Sometimes, they were asked: What is the reason that leads you to drive under the influence? If they answered Rarely or Never, they were asked: What is the reason you rarely or never drive under the influence? In both cases, respondents had the option of an open answer.

Later they were asked to rate from 0 to 10 the risk that driving under the influence of alcohol can cause a traffic accident in their opinion (0 being the minimum risk and 10 the maximum risk of crash).

Then they were asked to rate from 0 to 10 the harshness with which they thought DUI sanctions should be administered.

They were also asked: Is driving exceeding alcohol limits punishable? In this case, participants had the chance of answering Yes or No . We would then compare the correct answers with the standard to determine the knowledge.

Drivers who were unaware that DUI is punishable were asked about the probability of being sanctioned for this reason using the following question: When driving exceeding the limits of alcohol, out of 10 times, how many times is it usually sanctioned?

Another question dealt with the type of penalties. The participants were asked if the penalties for DUI consisted of economic fines, imprisonment or license suspension, either temporary or permanent. The question raised was: Have you ever received any penalty for driving under the influence? Possible answers were Yes or No . Those drivers who answered affirmatively were then asked about the harshness of punishment: How do you consider the punishment for DUI? The response options were Hard enough, Insufficient or Excessive. Furthermore, they were asked whether or not they changed their behavior after the punishment.

The questionnaire was used to ascribe drivers to different groups according to demographic and psychosocial characteristics, as well as to identify driving habits and risk factors.

Demographic variables

Gender: male or female.

Age: 14-17, 18-24, 25-29, 30-44, 45-65 and over 65 years old.

Educational level.

Type of driver: professional or non-professional.

Employment status: currently employed, retired, unemployed, unemployed looking for the first job, homemaker or student.

Driving habits

Frequency: the frequency with which the participant drive, the possible choices being Every day, Nearly every day, Just weekends, A few days a week, or A few days per month.

Mileage: the total distance in number of kilometers driven or travelled weekly, monthly or annually.

Route: type of road used regularly, including street, road, highway or motorway, and tollway.

Car use: motives for car use, for instance, to work, to go to work and return home from work or study centre, personal, family, recreational, leisure and others.

Experience/risk

Experience: number of years the participant has held a driver license, grouping them as 2 years or less, 3-6, 7-10, 11-15, 16-20, 21-25, 26-30 and over 30 years.

Traffic offenses. Number of sanctions in the past three years (none, one, two, three or more).

Accidents. Number of accidents as driver throughout life (none, one or more than one), and their consequences (casualties or deaths, or minor damages).

Once data were collected, a number of statistical analyses were performed, using the Statistical Package for the Social Sciences (SPSS), in order to obtain relevant information according to the aims of the study.

74.7% of the sample said that they had never driven under the influence. 15.5% of drivers said they did it almost never, and only the remaining 9.7% (sometimes 9,1%, often 0,2% or always 0,5%) acknowledged that they had driven after consuming alcohol (Figure  1 ).

Frequency of DUI.

Regarding the main reasons that led the drivers to act this way, expressed among drivers who admitted to having driven under the influence of alcoholic beverages, 24.5% of them indicated that it was unavoidable, as “I had to go home and couldn’t do anything else”, while 17.3% claimed that the act of drink-driving was an unintentional consequence or “something associated with meals”, and only 16.4% admitted having done it “intentionally”. In addition, 12.7% considered that “alcohol doesn’t impair driving” anyway (Figure  2 ).

Reasons for DUI.

“In any case, 60% of the interviewees perceived driving under the influence of alcohol as the highest risk factor for traffic accidents.”

Among them, the perception of this risk (or dangerousness of driving under the influence) is greater in women [F (1, 1081) = 41.777 p <0.05], adults aged between 18 and 44 [F (5, 1075) = 4.140 p <0.05], drivers who have never been fined for this infraction [F (2, 1080) = 29.650 p <0.05], drivers who had never committed the offense [F (4, 1077) = 40.489 p <0.05], and drivers who have never been involved in an accident [F (1, 1081) = 12.296 p <0.05]. Table  1 shows the values for this perception by gender and age.

There appears to be no significant relationship between the perceived risk attributed to DUI and other variables such as educational level, type of driver, driving frequency, vehicle use and years of experience.

The main reasons put forward for not drinking and driving included not drinking in any circumstances (50,5%), to avoid accidents (28,3%) as opposed to avoiding sanctions (10,4%) - such as financial penalties (8,4%), withdrawal of driving license (1,8%) or jail (0,2%) - or other reasons related to attitudes to road safety (16,6%).

On a scale of 0-10, participants rated the risk of economic penalties when driving under the influence of the alcohol with an average of 5.2, in other words they estimate the probability of being fined as roughly half of the times one drives drunk.

The perception of this risk (penalty or financial punishment for driving under the influence) is also greater in women [F (1, 1095) = 30,966 p <0.05], drivers who have never been involved in an accident [F (1, 1095) = 8.479 p <0.05], and drivers who had never been fined for this infraction [F (2 1094) = 12.515 p <0.05].

There appears to be no significant relationship between the perceived risk of financial penalty and other variables such as educational level, employment, type of driver, driving frequency, vehicle use and years of experience.

Almost everyone (99.1%) thinks that DUI is punishable and only 0.9% of drivers think it is not.

On a scale of 0-10, participants assigned an average of 9.1 to the need to punish this traffic breach severely. The score is higher in women [F (1, 1086) = 29.474 p <0.05], adults aged 18 to 24 years [F (5, 1089) = 2.699 p <0.05], drivers who have never been involved in an accident [F (1, 1095) = 8.479 p <0.05], and people who had never been fined for this reason [F (2, 1085) = 26,745 p <0.05], which means that these groups are less tolerant of this kind of behavior. By age, college students are the least tolerant and retirees are the most tolerant.

There was no significant relationship between the perceived need to punish this behavior harshly and variables such as type of driver, driving frequency and vehicle use.

Regarding the type of sanctions, 89.5% of drivers think that driving under the influence is subject to an economic fine, almost 70% say it could even be punished by imprisonment, while 96.4% believe it can lead to a temporary or permanent suspension of the license (Figure  3 ).

Type of sanction the driver think DUI is subject to.

Among the drivers who had been fined for DUI, nearly 75% considered that the imposed punishment was adequate, while the remaining 25% saw it as excessive (Figure  4 ). Finally, 91.7% of this group found they had changed their behavior after punishment (Figure  5 ).

Perception of punishment harshness imposed for DUI.

Perception concerning behavior change after punishment for DUI.

Alcohol is a major risk factor in traffic accidents. From the objective standpoint, alcohol interferes with the skills needed to drive safely, as evidenced by numerous studies on driving under the influence of alcohol conducted to date. From the subjective point of view, drivers also perceive it as dangerous, as our study shows.

Around 60% of respondents believe that driving under the influence of alcohol is maximum risk behavior. A smaller percentage compared to those reported by other studies in which the percentage of people that saw drink-driving as a major threat to safety reached 81% [ 15 ].

First, we note a clear correlation between perceived risk and avoidance behavior. In general the higher the perceived risk, the lower the probability of committing the offense, and vice versa: the lower the perceived risk, the greater the likelihood of driving after consuming alcohol.

Thus, drivers who do not commit this offense perceive that the risk of accidents associated with DUI is very high. When it comes to drivers who commit the offense occasionally, the perceived risk is lower, and when it comes to drivers who often drive under the influence of the alcohol, the perception of risk is clearly inferior. Thus, the frequency of DUI and risk perception seem to be inversely related.

These results are related to the hypothesis of optimistic bias, which states that drinkers are overly optimistic about probabilities of adverse consequences from drink. In a study [ 16 ] about overconfidence about consequences of high levels of alcohol consumption, the authors established an alternative to the optimism bias hypothesis that could explain our findings, affirming that persons who drink frequently and consume large amounts of alcohol daily could be more familiar with the risks of such behaviors.

Another important finding is that the risk perception of traffic accident as a result of DUI is influenced by variables such as sex and age. In relation to gender, the perception of risk seems to be higher in women than in men. In relation to age, risk perception is higher in adults between 18 and 44 years old.

The finding about the reason for not drinking and driving supports the already evident need for an integrative approach to developing sustainable interventions, combining a range of measures that can be implemented together. In this way, sustainable measures against alcohol and impaired driving should continue to include a mix of approaches, such as legislation, enforcement, risk reduction and education, but focus efforts more closely on strategies aimed at raising awareness and changing behavior and cultural views on alcohol and impaired driving.

Almost all the drivers surveyed are well aware that driving after drinking any alcoholic beverage is a criminal offense. They also consider that this is a type of infraction that should be punished harshly. In this respect, they assign nine points on a scale of ten possible.

Finally, with regard to the type of sanctions, 90% of drivers think that driving drunk is punishable by a fine. 96.4% consider that it may result in temporary or permanent suspension of driving license, and 70% believe that it can be punished with imprisonment.

In any case, there are several limitations of this study. This was a population-based study of Spanish drivers; there is possibly a lack of generalizability of this population to other settings.

Another possible limitation of this study is the use of self-report questionnaires to derive information rather than using structured interviews. Similarly, self-reported instruments may be less accurate than objective measures of adherence as a result of social desirability bias.

In Spain, various traffic accident prevention programs have been implemented in recent years. Some of them were alcohol-focused, designed to prevent driving under the influence and to inform the Spanish population about the dangers associated with this kind of risk behavior.

As a result, many Spanish drivers seem to be sensitized to the risk of driving drunk. As revealed in our survey, many Spanish drivers never drive under the influence of alcohol, and many of them identify DUI as maximum risk behavior. This shows that a high percentage of the Spanish population know and avoid the risks of DUI.

In any case, the reality is far from ideal, and one out of four drivers has committed this offense at least once. When asked why they did it, the two major risk factors of DUI we identified were the lack of an alternative means of transport and the influence of meals on alcohol consumption. Both situations, especially the latter, occur frequently, almost daily, while it is true that the amount of alcohol consumed in the former is considerably higher and therefore more dangerous.

In addition, most drivers are aware of the dangers of driving under the influence, and they tend to avoid the risk of accident or penalty for this reason. Some drivers never drive under the influence, to avoid a possible accident. To a lesser extent, some do not drive under the influence to avoid a possible fine. They usually think that the possibility of sanction in the event of DUI is so high that they will be fined every two times they risk driving drunk.

Moreover, drivers know the legislation regulating DUI and they believe that the current penalty for DUI is strong enough. Nevertheless, even though almost all the drivers that were fined for this reason say they changed their behavior after the event, nine out of ten drivers would penalize this kind of offense even more strongly.

Knowing how alcohol consumption impairs safety and driving skills, being aware of the associated risks, knowing the traffic regulations concerning DUI and penalizing it strongly are not enough. Many drivers habitually drive after consuming alcohol and this type of traffic infraction is still far from being definitively eradicated.

Additional efforts are needed for better management of a problem with such important social and practical consequences. Efforts should be focused on measures which are complementary to legislation and enforcement, increasing their effectiveness, such as education, awareness and community mobilization; Alcolock™; accessibility to alcohol or brief interventions.

Abbreviations

• Driving under the influence

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Acknowledgements

The authors wish to thank the Audi Corporate Social Responsibility program, Attitudes, for sponsoring the basic research. Also thanks to Mayte Duce for the revisions.

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Francisco Alonso, Juan C Pastor & Cristina Esteban

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All authors contributed to the design of the study and also wrote and approved the final manuscript. FA drew up the design of the study with the help of CE; the rest of the authors also contributed. JCP and LM were in charge of the data revision. JCP and CE also drafted the manuscript. FA performed the statistical analysis. All authors read and approved the final manuscript.

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Alonso, F., Pastor, J.C., Montoro, L. et al. Driving under the influence of alcohol: frequency, reasons, perceived risk and punishment. Subst Abuse Treat Prev Policy 10 , 11 (2015). https://doi.org/10.1186/s13011-015-0007-4

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Exactly how dangerous is drink driving? An examination of vehicle crash data to identify the comparative risks of alcohol-related crashes

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This study aimed to explore how specific situational variables (remoteness, speed zones, days of the week, hours of the day) and risk factors (risky behaviours and road-related conditions) might influence the comparative likelihood and severity of alcohol-related crashes (ARCs). Vehicle crash data ( N  = 63,226) were analysed and included the details of crashes between 2015 and 2019. In comparison to non-ARCs, ARCs were up to two times more likely to occur in rural areas and on weekdays, and two to four times more likely in the late evening and early morning (6 p.m. to 6 a.m.). In addition, risky road conditions and driving behaviours were found to significantly increase both ARC likelihood and severity, with some factors (e.g., speeding) increasing fatality risk by up to nine times. Overall, this study highlights the specific risks associated with drink driving and highlights situational factors that may increase crash risk.

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Introduction

Despite a large body of research having investigated the antecedents of drink driving offending to inform deterrence initiatives (Freeman et al. 2021a , 2020 , 2016 ), the behaviour remains an ongoing safety risk among all roads and communities. Among Australian drivers specifically, drink driving is attributed to approximately a quarter of all crashes and half of fatal crashes in Australia (Road Safety Commission 2021 ; Department of Transport and Main Roads 2021 ; Transport Accident Commission 2021 ). Within Australian jurisdictions, drink driving is a criminal offense and drivers must be under a blood alcohol concentration (BAC) of 0.05 g per 100 ml of blood. Nonetheless, recent statistics (Freeman et al. 2021b ) have shown that a relatively large proportion of Queensland drivers have driven when they thought they may have been over the legal alcohol limit (22.1%) and when they were knowingly over the limit (15.9%). With consideration to the high proportion of self-reported drink driving offending and the increased fatality risk of alcohol-related crashes (ARCs), the issue continues to highlight a need for further research to help reduce offending frequency and crash severity.

Characteristics of drink drivers

Drink driving research has thus far focused on the perceptual and behavioural factors of individuals that are associated with drink driving intentions. Such research has generally concluded that factors such as: risk perceptions of harm and apprehension, perceptions of impairment, social influences, alcohol use frequency, past offending, experiences with punishment avoidance, exposure to sanctions, dimensions of personality (e.g., sensation seeking; psychopathy), and attitudes towards sanctions; in part, play a role in the intentions to offend (Fernandes et al. 2010 ; Freeman et al. 2020 , 2021b ; Szogi et al. 2017 ; Watson et al. 2017 ; Freeman and Watson 2009 ; Stringer 2021 ; Hatfield et al. 2014 ). However, while it is important to continue building an understanding of the precursors that influence drink driving behaviours, it is also crucial to identify the situational factors associated with an increased risk of crashing once the behaviour has been engaged. Such research may inform the procedures around policing the behaviours but may also highlight areas of focus for infrastructural change or community safety awareness campaigns.

As opposed to experiential and psychological risk factors, a number of studies have identified demographic (e.g., age) and situational (e.g., location) risk factors to drink driving. In particular, males have demonstrated to be over-represented within drink driving populations (Freeman et al. 2020 ), although the tendency for women’s drink driving behaviours has also been noted (Armstrong et al. 2014 ). Secondly, older drivers have been noted to be at a higher risk of offending (Goldenbeld et al. 2020 ), with the average drink driver age reportedly being between 36 and 40 years old (Davey et al. 2020 ; Freeman et al. 2021c ); younger cohorts are proportionally more prevalent among fatal crashes, due to inexperience with both driving and alcohol (DTMR 2015 ). Such statistics suggest that the characteristics of drink driving populations may not be entirely translatable to the risk of crashing when under the influence of alcohol. Alternatively, reports of random breath testing (RBT) data have also identified the remoteness of a location and the time of day as situational factors of concern, in that drink driving is proportionally more frequent and done at higher BAC levels, in rural areas and at night between 6 p.m. and 6 a.m. (Armstrong et al. 2017 ).

How alcohol increases crash risk

Given the high proportion of ARCs within the crash data, investigators have also attempted to understand how alcohol may affect driving performance. To date, it has been established that alcohol consumption can greatly reduce divers’: ability to judge speed and distance, psychomotor functioning, co-ordination, attentional control, and reaction speed (Christoforou et al. 2013 ; Rakauskas et al. 2008 ; Zhao et al. 2014 ; Wester et al. 2010 ; Jongen et al. 2014 ). More specifically, studies have indicated that BAC has a negative relationship with the performance of driving-related factors (Martin et al. 2013 ). For example, several driving simulator studies have shown delayed reaction times when under the influence of alcohol, compared to non-alcohol conditions (Yadav and Velaga 2019 ). In fact, a 10% increase in BAC has been shown to lead to a 2% increase in reaction times (Christoforou et al. 2013 ). Such findings suggest that drink drivers may be more at risk when driving, particularly when in hazardous conditions, such as slippery roads or driving in poor lighting.

Another factor to consider is that alcohol has been evidenced to influence an increased tendency to take risks (Fromme et al. 1997 ; McMillen and Wells-Parker 1987 ). A study of coroner data by Freeman et al. ( 2021c ) demonstrated that crashes involving alcohol and drugs also tend to have higher rates of other risky behaviours, such as speeding and reckless driving. This may mean that drink drivers are at significantly higher risk of crashing, not only because they could be more likely to engage in other risky driving behaviours (e.g., speeding, mobile phone use and fatigued driving), but also because they have a reduced ability to drive. One study looked at the impact that the combined effects of alcohol and distraction would have towards driving ability, and showed that alcohol and distraction produced amplified adverse effects on driving ability (Rakauskas et al. 2008 ). Regardless of the dangers associated with drink driving, no such studies have investigated the increased risk that the behaviour may have on safety, when accounting for the combination of risky driving behaviours and unfavourable road conditions.

The present study

In summary of the current drink driving literature, there has been strong support given by research studies in identifying the antecedents of intentional drink driving behaviour. However, very little research has been afforded to the investigation of crash data, which could highlight the increased risks that are associated with drink driving. Identifying the comparative risks of specific situational factors may help inform road authorities to designate finite resources more appropriately. Therefore, the primary purpose of this study was to explore the comparative risks associated with ARCs. Specifically, it first aimed to explore how specific situational variables (remoteness, speed zones, days of the week, hours of the day) and risk factors (risky behaviours and road-related conditions) might impact on the likelihood and crashing, compared to non-ARCs. Secondly, it was aimed to examine how the situational and risk factors might increase ARC injury severity and fatality risk. Thirdly, this study aimed to investigate the fatality risk of ARCs when engaging in other risky behaviours (i.e., speeding, fatigued driving, distraction, and being unrestrained), across specific situational conditions.

This study used data provided by the Department of Transport and Main Roads (DTMR), Queensland, which contained information related to road crash data ( N  = 63,226) that occurred during the years 2015 to 2019. All cases of data were included, and no missing coding was found. Five years of data were used to: (a) ensure an adequate sample of ARCs was collected; and (b) ensure the sample was sufficient in capturing the true impact of factors associated with drink driving crashes and reduce the effect of time specific anomalies that may have occurred between years. Overall, ARCs represented 5.7% ( n  = 3626) of all vehicle crashes in Queensland between 2015 and 2019. Five years of crash data allowed a comprehensive analysis to identify necessary trends and make comparisons between ARCs and non-ARCs, but also likely remained relevant to the current generational trends. Crash data are traditionally collected from an attending police officer, who records the crash characteristics and any potential causal factors that might have been present. More serious crashes (i.e., resulting in fatal or serious injuries), however, can be attended to by a forensic crash investigation unit, in which trained officers will use an in-depth investigation to inform their report of the crash characteristics and causal factors.

The variables of interest for this study were related to characteristics and causal factors that may be relevant to ARCs. Situational variables included: time of day, period of week (i.e., weekday; weekend), level of remoteness (i.e., rural, suburban, urban), and the speed zone (≤ 50 kmph, 60–70 kmph, 80–90 kmph, and 100–110 kmph). In addition, a number of dichotomously scored variables identifying whether certain factors were present in the crash were also utilised and included: drink driving, speeding behaviours, fatigue, distraction (e.g., inattention and mobile phone use), whether the road was unsealed, whether the road was slippery, whether visibility was low (i.e., rain and fog), and whether the road was poorly lit (e.g., at night). Finally, injury severity was also used as a means to determine the severity of the crash (1—minor injury, 2—injury requiring medical treatment, 3—injury requiring hospitalisation, and 4—injury leading to fatality).

Data analysis

The data were analysed using statistical analysis software SPSS (version 28). First, cross-tabulations and chi-square tests were used to examine and compare the differences in ARC and non-ARC proportions among the situational variables (i.e., where and when) and risk factors (i.e., behaviours and road conditions). A factor of comparative likelihood was then calculated by dividing the relative proportions of ARCs into non-ARCs for each variable, giving a value indicating the likelihood that the variables would relate to ARCs, compared to non-ARCs. Next, analysis of variance (ANOVA) tests were used to examine the differences in injury severity of ARCs amongst the situational variables and risk factors. Browns–Forsythe ( BF ) statistics were reported for variables violating homogeneity of variance assumptions, and effect sizes were interpreted based on Cohen’s ( 1988 ) for ANOVAs (small = 0.01, medium = 0.06, large = 0.14). Further, a logistic binomial regression was used to determine the impact that relevant situational variables and risk factors were having on the fatality rates of ARCs. The findings from the univariate ANOVAs were used to retrospectively inform which variables were statistically relevant for the analysis. Frequencies were also used to help determine an appropriate classification cut-off for the analysis. Finally, to identify the comparative fatality risk of each risky behaviour across the situational data, proportions and comparative likelihood statistics were calculated across ARCs.

The comparative likelihood of ARCs and non-ARCs

As discussed, cross-tabulations and chi-square analysis (Table 1 ) were used to highlight the comparative risk of ARCs, compared to non-ARCs. When looking at the level of remoteness, the results revealed that despite the majority of ARCs occurring in urban (51.9%) and suburban (42.3%) areas, rural areas (5.8%) had a higher proportion of ARCs that ended in fatalities (17.5%), compared to suburban (7.6%) and urban (4.1%) areas. The spread of ARCs was also shown to be significantly different than non-ARCs ( χ 2  = 314.68, p  < 0.001), particularly in rural areas, which were more than two times more likely to involve ARCs compared to non-ARCs. Concerning speed zones, frequencies showed that the highest frequency of ARCs occurred in 60–70 kmph speed zones (59.9%), followed by ≤ 50 kmph zones (22.2%), 100–110 kmph zones (9.7%), and then 80–90 kmph speed zones (8.9%). Chi-square tests indicated that there was some discrepancy in the proportions between ARCs and non-ARCs ( χ 2  = 163.36, p  < 0.001), in that ARCs tended to be more common in higher speed zones than non-ARCs. Subsequently, ARCs were slightly less likely to occur in speed zones of 70 kmph and below, but slightly more likely to occur in higher speed zones.

Next, comparative differences among time periods were examined, and it was shown that despite weekends having significantly fewer days compared to weekdays, a comparable proportion of ARCs were present (61.7%). Chi-square tests ( χ 2  = 1,001.55, p  < 0.001) confirmed that compared to non-ARCs, ARCs were more than two times more likely to occur during the week and more than 1.5 less likely to occur on weekdays. The results also demonstrated that the majority of ARCs were occurring in the late evening (42.6%; 6 p.m. to 12 a.m.), followed by early morning (27.4%; 12am to 6am), early evening (20.9%) and late morning (9.0%). Comparative analysis indicated that there were large disparities in proportions between non-ARCs and ARCs ( χ 2  = 4,512.31, p  < 0.001), in that ARCs were more than four times more likely to occur in the early morning, and nearly three times more likely in the late evening, and thus were subsequently less likely to occur in other time periods, compared to non-ARCs.

The crash proportions were also assessed among behavioural and road-related risks. Proportional statistics showed that firstly, all of the risk factors were identified as more common in ARCs than in non-ARCs. Most notably, speeding and poor lighting were found to be approximately five times more likely to be contributing factors, not wearing a seatbelt was four times more common, and fatigue and driving on an unsealed road were approximately two times more common among ARCs than in non-ARCs. Follow-up chi-square tests revealed that these differences were statistically significant in all cases ( χ 2  = 4.68 to 2,013.46, p  < 0.001 to 0.031), except for distraction.

The risk of situational factors and behaviours towards ARC injury severity and fatality risk

ANOVAs were used to compare the risk of situational variance toward ARCs (Table 2 ). Firstly, preliminary analysis demonstrated that ARCs were significantly more dangerous than non-ARCs ( BF (1, 4079)  = 977.80, p  < 0.001, η 2  = 0.015, Δ M  = 0.38). When investigating locational differences, it was shown that ARC injury severity ( BF (2768)  = 21.95, p  < 0.001, η 2  = 0.013) was significantly different between location types. Post-hoc Bonferroni tests revealed that rural locations contained ARCs that had higher injury severity, compared to suburban (Δ M  = 0.14, p  = 0.016) and urban (Δ M  = 0.27, p  < 0.001) locations; and suburban locations contained ARCs that were higher in injury severity compared to urban areas (Δ M  = 0.13, p  < 0.001). In addition, ARC injury severity marginally differed across speed zones ( BF (3, 2077)  = 2.62, p  = 0.049, η 2  = 0.002), with Bonferroni post-hoc comparisons showing the only difference to be between 100 kmph zones and 80–90 kmph zones (Δ M  = 0.12, p  = 0.043). However, further ANOVAs indicated that there was no significant difference in ARC injury severity between days of the week ( F (1, 3624)  = 0.00, p  = 0.972, η 2  = 0.000) and days of the day ( BF (3, 2114)  = 1.44, p  = 0.230, η 2  = 0.000).

ANOVAs also indicated that there was significant higher injury severity when a number of risky conditions and behaviours were present, including: speeding ( BF (1, 598)  = 105.80, p  < 0.001, η 2  = 0.031, Δ M  = 0.37), fatigue ( BF (1, 373)  = 23.84, p  < 0.001, η 2  = 0.006, Δ M  = 0.20), no seatbelt ( BF (1, 336)  = 103.48, p  < 0.001, η 2  = 0.025, Δ M  = 0.41), poor lighting ( BF (1, 1680)  = 93.71, p  < 0.001, η 2  = 0.023, Δ M  = 0.24). In contrast, ARC severity was slightly less severe when distraction ( BF (1, 741)  = 19.15, p  < 0.001, η 2  = 0.005, Δ M  = − 0.14), low visibility ( BF (1, 177)  = 5.56, p  = 0.019, η 2  = 0.002, Δ M  = 0.10), and slippery roads ( BF (1, 659)  = 4.58, p  = 0.033, η 2  = 0.001, Δ M  = − 0.08) were present factors. Proportional statistics of ARC severity types across the situational factors are also provided in Table 2 (right) to illustrate the spread of ARCs.

Finally, a binary logistic regression (Table 3 ) was used to investigate how the situational and risk factor variables might impact on the fatality rating of ARCs. Only those variables that showed to be statistically relevant to injury severity at the univariate level were included. Specifically, given the large sample size and capability to detect arbitrary effect sizes, only those variables with significance values of < 0.001 were included, as the associated effect sizes of variables outside of this were negligible (e.g., η 2  ≤ 0.002). To first identify an appropriate cut-off level, frequencies were run on the fatality rates of ARCs and revealed that 6.3% ( n  = 230) of ARC crashes were fatal. The model was run accordingly and was found to be significant ( χ 2  = 346.10, Nagelkerke R2 = 0.242, p  < 0.001) and a good fit to the data (Hosmer and Lemeshow = 3.66, p  = 0.722). The model demonstrated to accurately classify a non-fatal crash 82.7% of the time, and correctly classify a positive result 66.6% of the time (overall = 81.7%). Individually, level of remoteness was found to decrease the risk of fatality by approximately half for each step up in population ( B  = − 0.59, p  < 0.001, odds = 0.56), excessive speeding was found to increase the risk of ARCs being fatal by approximately nine times ( B  = 2.16, p  < 0.001, odds = 8.69), fatigue by one and a half times ( B  = 0.51, p  = 0.023, odds = 1.67), not wearing a seatbelt by approximately four times ( B  = 1.37, p  < 0.001, odds = 3.95), and poor lighting by one and a half times ( B  = 0.45, p  < 0.007, odds = 1.57). Conversely, distraction was found to reduce the risk of a fatality among ARCs by 84% ( B  = − 1.87, p  < 0.001, odds = 0.16).

The comparative risk of fatality among risky driving behaviours in specific situational conditions

Finally, proportions and the comparative likelihood were calculated to assess how risky behaviours may impact on the fatality rating of ARCs in certain situational conditions (Table 4 ). Firstly, the results showed that the increased risk of speeding was high throughout all situations, with comparative risk ratings between + 1.85 to + 5.41. However, speeding increased the fatality rating of ARCs the most in urban zones (+ 5.17), in low-speed zones (+ 5.07), on weekends (+ 5.41), in the late evening (+ 4.84) or late morning (+ 4.72), and on slippery roads (5.05). Next, fatigue was also shown to represent an increased ARC fatality risk, although the range between variables was smaller (+ 1.20 to + 3.58). Specifically, the highest fatality risk was in suburban areas (+ 1.82) in 60–70 kmph zones (+ 2.85), on weekends (+ 2.29), in the early morning (+ 2.11) or late evening (+ 2.00), and on slippery roads (+ 3.58).

Not wearing a seatbelt also dramatically increased the fatality risk of ARCs with a comparative risk range of − 1.35 to + 6.90. In particular, fatigue posed the biggest comparative fatality risks in suburban areas (+ 4.25), in lower speed areas (+ 4.88 to + 5.02), on weekdays (+ 4.61), in the early evening (+ 5.10), and in areas of low visibility (+ 6.90). Notably, there appeared to be a reduced risk of fatality in the late morning (− 1.35), although this is likely due to a low number of fatal ARCs under these conditions. Finally, as per previous results, distraction was found to reduce the fatality risk of ARC, with scores showing that distraction reduced the risk of fatalities by 2.10 to 10.92 times. Distraction had the largest reduced fatality risk in urban areas (− 10.92), both low (no fatal ARCs) and high-speed zones (− 4.27), on weekdays (− 5.00), in the late evening (− 6.67), and on unsealed roads (no fatal ARCs).

The comparative risk of ARCs

The primary purpose of this study was to identify the increased situational risks involved with drink driving in Queensland, Australia; and to highlight what factors may increase the severity and fatality of ARCs. The findings indicated that the proportion of ARCs across locational, time-related and risk-related factors, differed significantly from non-ARCs. These differences were at times dramatic, as some situational factors were up to four times more likely to be present in ARCs than non-ARCs. Consistent with previous RBT data (Armstrong et al. 2017 ), the severity of crashes was significantly higher in rural areas, but far more common in populated areas. This highlights that greater police presence (and deterrence) may be required in all areas, but proportionally more in rural locations. Recent research suggests that the mere exposure to police roadside testing may be enough to create a deterrent effect among impaired drivers (Mills et al. 2022 ; Freeman et al. 2021b ), and thus periodic high visibility operations in remote, which are not accustomed to heavy police enforcement, may be beneficial.

Alike non-ARCs, ARCs were observed to be more common in areas of lower speed zones (< 70 kmph). This trend is possibly because these areas contain a greater demand on the driving task (e.g., intersections; other road users; pedestrians) and drink drivers have an impaired cognitive (driving) ability (Christoforou et al. 2013 ; Rakauskas et al. 2008 ; Zhao et al. 2014 ; Wester et al. 2010 ; Jongen et al. 2014 ). However, another consideration might be related to the quantity of low-speed zones, compared to high-speed zones (i.e., a simple exposure effect). A final explanation was that speeding (a notable risk factor) was observably more common and fatal among ARCs in slower speed zones and urban areas. In fact, speeding was attributable to more than half of fatal ARCs in these areas. Also concerning was the high prevalence of drivers being unrestrained in fatal ARCs. Although the prevalence may have been higher in rural areas, the fatality rating of ARCs involving unrestrained drivers were again higher in more populated areas and lower speed zones.

Together, the findings highlight how specific situational variance impacts on different levels of risk. For example, while there is limited police presence (and thus proportionally higher offending rates) in more remote areas, there is also less environmental variables (e.g., pedestrians; intersections; stimuli) to consider and navigate when drink driving. Conversely, more populated areas may contain comparatively lower rates of offending but are more difficult to navigate when impaired and have significantly more environmental risks to consider.

In regard to time periods, the results showed that ARCs were more likely to occur on weekend days, and between the hours of 6 p.m. to 6 a.m., which is again consistent with previous findings from RBT data (Armstrong et al. 2017 ). One unexpected finding, however, is that unalike ARC likelihood, injury severity rarely deviated between the variables. This may be because ARCs were shown to be generally more severe and thus, less variation is found between the variables, or that underlying factors are introducing unforeseen risks. The results suggested that different risky behaviours were more prominent at different times of the day, and impacted the fatality risk of ARCs, which may in part explain the lack of variance. For example, fatigue was shown to be significantly more prevalent between the hours of 6 p.m. to 6 a.m., which may in part reduce risks during the day. However, there is also a significant increase in pedestrian and vehicle traffic volume, which may seemingly counterbalance the apparent risk, and confound broader variables, such as time periods. Nonetheless, further research on this area would help better understand the interactions between time, situational variance and crash risk.

Risky road-related conditions, such as lighting, and the road surface were shown to interact with the likelihood of ARCs (up to four and half times) and the associated injury severity. Such findings again suggest that drink drivers’ may be less likely to adequately meet situations requiring heightened attentional processing. This supports previous findings that alcohol can greatly reduce cognitive performance while driving (Christoforou et al. 2013 ; Rakauskas et al. 2008 ; Zhao et al. 2014 ; Wester et al. 2010 ; Jongen et al. 2014 ).

In conjunction, the results also showed that risky behaviours such as speeding and driving fatigued were significantly more common (two to five times more likely) among ARCs than for non-ARCs. This finding implies that drink drivers are at an exponential risk of crashing because they may be more likely to engage in several other risk-related behaviours, while also being cognitively impaired. As discussed, alcohol consumption has been linked to an increased tendency to take risks (Fromme et al. 1997 ; McMillen and Wells-Parker 1987 ), and crashes involving alcohol have been associated with other risky behaviours, such as speeding and reckless driving (Freeman et al. 2021c ). This exponential risk was highlighted in the current findings, as risky driving behaviours were shown to increase the fatality risk (up to seven times) of ARCs when risky road conditions were present.

In contrast, road conditions such as slippery roads and low visibility (e.g., rain; fog) were shown to decrease the severity of ARCs. However, previous research has shown that drivers adopt safer driving behaviours in weather conditions (e.g., driving slower), due to increased risk perceptions related to crashing (Ahmed and Ghasemzadeh 2018 ; Cai et al. 2016 ). Therefore, these regulatory behaviours may also be present in drink drivers, who would be exponentially impaired in such conditions, despite their increased propensity for risk (Fromme et al. 1997 ; McMillen and Wells-Parker 1987 ). Previous research has shown that cannabis users adopt safer driving behaviours when under the influence in order to reduce the associated risks of intoxication (Arkell et al. 2020 ). While the evidence is sparse, this may be similar for alcohol users, who have noted the aversive effects of alcohol on their driving (Love et al. 2022 ).

Similarly but unexpectedly, distraction was also shown to reduce the odds of an ARC being fatal by as much as 94%, which is not supportive of previous research suggesting that distracted driving increases crash risk (Choudhary et al. 2020 ), particularly when paired with drink driving (Rakauskas et al. 2008 ). This discrepancy may have been due to underlying factors, such as that mobile phone use may be more likely to be combined with drink driving in lower speed areas or safer circumstances. As previously highlighted, mobile phone offenders regulate their driving and phone use to meet their perceived risk involved with the activity (Oviedo-Trespalacios et al. 2017 ). Alternatively, distraction is a difficult behaviour to determine in crashes, and it may be that attending officers were less likely to record distraction as a contributing factor in the circumstances where alcohol was already attributed to the crash. Nonetheless, further research is needed to identify both the prevalence rates of these combined behaviours and the increased risk that the collective effects might have toward driving ability.

Implications, limitations, and future suggestions

Overall, the findings of this study have highlighted particular situational variables, driving behaviours and road conditions that represent an increased crash and fatality risk for ARCs, and therefore may inform intervention strategists on a more targeted and effective countermeasure to drink driving offenders. While the crash data have indicated that drink driving represents an increased crash risk in general, it has also highlighted specific situations that represent higher crash quantities (e.g., urban areas), higher crash proportions (e.g., weekends), and higher risk for injury (e.g., rural areas) for drink drivers. There also appears to be other risky behaviours (e.g., speeding) that may be more common in specific situational driving conditions (e.g., remote areas), further mediating the level of risks involved. Therefore, to most efficiently reduce the fatality figures associated with drink driving, interventions should place particular focus towards these areas and populations (in addition more generalised approaches).

Specifically, it may be useful to delegate finite police resources to areas and populations of concern, at specific times. Alternatively, media-based interventions or educational training aimed at increasing the stigmatisation of drink driving can use the risk-based statistics outlined in this study to inform populations about the dangers of drink driving, and in particular, combining risky behaviours under specific circumstances. The findings demonstrated the potential for risk among the drink driving population, and by publicising the highlighted risk figures, both current and future drivers may develop more understanding of such risks. For example, a Japanese study showed that highly publicising ARCs and using media-based strategies may have reduced the number of ARCs, but also improved social norms and behaviour (Nakahara and Ichikawa 2011 ). In conjunction with this approach, designated driver programs, which aim to encourage designating a sober driver when drinking in a group, may be a useful strategy to help promote the use of safe driving practices surrounding alcohol consumption.

The statistics also indicated that the crash likelihood did not meaningfully increase when drink driving was a factor in some conditions. For example, lower-speed and more populated areas, on weekends, low visibility, slippery roads, and distraction were factors shown to have a lesser impact than others. Further research may wish to investigate whether there were little differences because these factors represent an increased risk to all drivers or whether other underlying factors (e.g., sober drivers are more likely to drive in populated areas; people drive safer when risk is present) are confounding on some effects. In addition, it would be beneficial to identify whether drink drivers adopt regulatory behaviours to reduce their perceptions of harm likelihood. While research has indicated that alcohol use is linked to risky behaviour, it may be enlightening to understand if there is some psychological variance between those who engage risk and those who negate it, after the drink driving has been initiated.

Despite the implications present, this study contained limitations and thus several future directions are suggested. Most notably, the data used contained only cases who have been involved in a traffic crash and is therefore not necessarily representative of the total drink driver population. Future research may therefore wish to make comparisons with other data sources from different jurisdictions. Similarly, it would be beneficial to investigate whether the characteristics of alcohol related infringement data matches the crash data to determine if those who drink drive and have crashed have similar dynamics to those who drink drive and get caught. Another limitation involved the reliance of police officers to report the apparent causes of the crash. Some factors may be likely to be reported as the primary crash indicator over some less observable (i.e., phone use) or objective (i.e., fatigue) factors. Self-report data may provide a contextual comparison, although this form of data is not without its own limitations.

Self-report methodologies involving the engagement of other risky behaviours while drink driving may further highlight how prevalent the magnified risk of combined offending is, specifically within the identified situational variables. Research focused on the deterrence of such offending behaviours may also shed further light on how this increased risk interacts with perceptions of impairment and risk among offenders. Finally, future research may benefit from investigations on how behavioural (e.g., speeding) and road-related (e.g., rain) risk factors may impact on specific cognitive performance indicators when under the influence of alcohol. Simulator-based studies may prove to be a beneficial avenue for this stream of research. In summary, this study has demonstrated that drink driving embodies a significant danger on the road, representing a relatively high portion of crashes (6.1%), due to an increased risk of crashing in specific conditions (up to five times). Further, the risk of being fatally injured is significantly amplified under certain circumstances (up two times) and when combined with other risky behaviours (up to nine times).

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Acknowledgements

We would like to thank the Department of Transport and Main Roads for supplying the data for analyses, and Dr Bonnie Huang for organising the data files.

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Love, S., Rowland, B. & Davey, J. Exactly how dangerous is drink driving? An examination of vehicle crash data to identify the comparative risks of alcohol-related crashes. Crime Prev Community Saf 25 , 131–147 (2023). https://doi.org/10.1057/s41300-023-00172-6

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Perspective

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Clinical Action against Drunk Driving

* E-mail: [email protected]

Affiliations Department of Medicine, University of Toronto, Toronto, Ontario, Canada, Evaluative Clinical Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada, Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada, Division of General Internal Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada, Center for Leading Injury Prevention Practice Education & Research, Toronto, Ontario, Canada

Affiliations Department of Medicine, University of Toronto, Toronto, Ontario, Canada, Institute for Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada, Department of Medicine, Mount Sinai Hospital and University Health Network, Toronto, Ontario, Canada

• Donald A. Redelmeier, 
• Allan S. Detsky

Published: February 14, 2017

• https://doi.org/10.1371/journal.pmed.1002231
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Citation: Redelmeier DA, Detsky AS (2017) Clinical Action against Drunk Driving. PLoS Med 14(2): e1002231. https://doi.org/10.1371/journal.pmed.1002231

Copyright: © 2017 Redelmeier, Detsky. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This project was supported by a Canada Research Chair in Medical Decision Sciences, the Canadian Institutes of Health Research, and the BrightFocus Foundation. No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: IRTAD, International Traffic Safety Data and Analysis Group; MADD, Mothers Against Drunk Driving; NHTSA, National Highway Traffic Safety Administration; OECD, Organisation for Economic Co-operation and Development

Provenance: Commissioned; not externally-peer reviewed

In 2014, over 100,000 people in the United States were hospitalized because of alcohol-related traffic crashes, and 9,967 died (exceeding the 6,721 US deaths from HIV in the same year) [ 1 , 2 ]. On 17 March 2017, the National Highway Traffic Safety Administration (NHTSA) plans to promote a safety campaign against drunk driving. Past estimates suggest that law enforcement against drunk driving reduces traffic fatalities by 20% and that high-probability detection is more effective than high-severity punishment [ 3 , 4 ]. Yet, 12 states in the US, including the large states of Texas and Minnesota, prohibit random sobriety checkpoints, and the remaining have uneven efforts against drunk driving [ 5 ]. This Perspective identifies some groups with a vested interest in preventing drunk driving, describes reasons for the relative inaction, and proposes more action by physicians.

Traditionally, physicians and allied health care providers have deferred to others about how to address the health risks of drunk driving. One explanation is that drunk driving is a behavioral choice, and behavioral change is difficult to effect in a time-limited clinical encounter [ 6 ]. Moreover, preventive care may provide less evident benefit to the patient than prescribing an acid blocker, for example, to treat symptomatic alcohol-induced gastritis. While a pregnant woman who drinks alcohol is likely to be warned by her obstetrician or midwife on the risks to fetal development, most patients in our experience who are prone to drunk driving are easily missed because physicians rarely ask about drunk driving, despite often asking about alcohol. As a consequence, standard care may fail to identify this prevalent, modifiable, and serious health risk.

Vehicle manufacturers are the most powerful commercial group that can promote traffic safety. Over time, this industry has carefully developed and marketed technologies to protect drivers, such as seat belts, airbags, antilock brakes, and safety glass. Currently, the main technology to prevent drunk driving is an ignition interlock that forces drivers to have a breath test before engine engagement. This device, now imposed only on the vehicles of convicted drunk drivers, is unlikely to be adopted broadly any time soon unless manufacturers want to boast that they make the safest cars for those prone to drunk driving. The net result is that vehicle regulators in the US are unable to rely on manufacturer innovations or economic forces to prevent drunk driving.

Other large groups have even less incentive to promote sobriety while driving. Alcohol manufacturers promote “responsible drinking,” which is a vacuous tautology because adverse events can be deemed “irresponsible” by rhetorical hindsight. Celebrities in the entertainment industry are occasionally charged with drunk driving yet rarely express enduring regret. Lawyers gain little financial benefit from deterring drunk drivers, whereas some profit substantially by defending those who have deep pockets and are charged with drunk driving. Individual police officers themselves sometimes consider traffic enforcement as low-prestige work with little career satisfaction [ 7 ]. Driving enthusiasts argue that enforcement mostly inconveniences safe drivers to catch a few deviants. Those caught driving drunk are rarely grateful for the penalties.

Sometimes medical science can inspire behavior change, and drunk driving could seem amenable to research because of the large number of incidents. A rigorous clinical trial, however, cannot be conducted unless broad regions are willing to implement interventions in a thorough manner. An epidemiological analysis contrasting different states would also be easily misinterpreted because of dissimilarities across regions and diversity within regions (for example, the risk of dying in an alcohol-related traffic crash is three times higher in South Carolina than in New Jersey even though both states allow random sobriety checkpoints) [ 1 ]. Because individuals are not randomly assigned to driving locations, furthermore, the confounders are almost boundless [ 8 ]. These research limitations mean that scientific evidence is unlikely to cause people to stop drunk driving [ 9 ].

Activism and Politics

The most prominent body advocating change has been Mothers Against Drunk Driving (MADD), a citizen group with deeply motivated members [ 10 ]. The mission of MADD is to “stop drunk driving, support the victims of this violent crime, and prevent underage drinking.” MADD has successfully pushed for drunk-driving laws, increased public awareness, designated-driver initiatives, alcohol ignition interlock programs, and victim impact panels. Yet, MADD is mostly a volunteer organization, with fewer than 500 employees. MADD has also been criticized for having administrative costs and for shifting towards broader prohibitions against alcohol consumption [ 11 ]. Ultimately, MADD has no power to enforce drunk-driving laws.

Politicians in the US seldom discuss traffic safety with the same zeal that they direct at debates on economic growth, domestic terrorism, public scandals, gun deaths, climate change, and other public-policy priorities. Indeed, making a political issue of drunk driving can carry particular risks because the historical failure of prohibition decades ago (1920 to 1933) means that a well-intentioned politician is easily ridiculed or mischaracterized as being antialcohol [ 11 ]. A US politician who seeks re-election will rarely promise action against drunk driving. The US political process, therefore, exchanges safety for freedom and tolerates a remarkably high rate of alcohol-related traffic fatalities relative to other countries ( Fig 1 ).

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Histogram of alcohol-related traffic fatalities in the US and other countries. The vertical axis shows ten countries sequenced by death rates. The horizontal axis shows alcohol-related traffic deaths as fatalities per million population annually. Data are from the Organisation for Economic Co-operation and Development (OECD) Road Safety Annual Report 2015 authored by the International Traffic Safety Data and Analysis Group (IRTAD) and available at the following website: http://www.oecd-ilibrary.org/transport/road-safety-annual-report-2015_irtad-2015-en . The death rates were calculated from Table 1.3 of the report and individual country alcohol profiles. The results show high rates of alcohol-related traffic fatalities in the US relative to other countries.

https://doi.org/10.1371/journal.pmed.1002231.g001

Medical Initiatives

Health care providers are perhaps the one remaining large powerful group with a profound commitment to health. Physicians and allied life-saving professionals sometimes advocate to reduce cigarette smoking, drug abuse, domestic violence, or other societal epidemics. Drunk driving causes major mortality and morbidity that is utterly preventable, unlike many advanced diseases. The losses are also tragic because offenders usually have no malicious intent yet many lives are irrevocably altered (including their own). Because the market forces for commercial industries run in a different direction, physicians could advocate more for what works against drunk driving ( Box 1 ) [ 12 ]. Civic advocacy, however, rarely leads to immediate gratification and sometimes deteriorates into dissenting backlash [ 13 ].

Box 1. Physician Strategies to Prevent Drunk Driving

• Alcohol screening and brief interventions for patients with alcohol problems
• Physician warnings for patients who sometimes drink and drive
• Treatment of patients diagnosed with alcohol dependence
• Counseling of patients not to ride with drunk drivers
• Supporting enforcement of laws against drunk driving
• Promoting sobriety checkpoints in local communities
• Lending voice to mass media campaigns against drunk driving
• Joining multicomponent interventions in coalitions of community group members

Patients do not want to become traffic statistics, tend to listen to their physicians, and take advice seriously. When asked about alcohol consumption, for example, patients often respond truthfully inside a private medical relationship. A simple extension, therefore, might be for physicians to also ask patients about past episodes of drinking and driving. Patients identified, in turn, could be recommended a taxi service, ride-sharing option (e.g., Uber and Lyft), or a designated-driver substitute. The intent is to suggest safer alternatives so patients who drink do not need to drive [ 14 ]. The intent is not to preach sobriety or to betray patient trust. Ideally, these harm-reduction strategies should be planned in advance because later inebriation will predictably impair judgment.

In Canada, recent financial incentives have been effective at motivating physicians’ warnings for medically unfit drivers and reducing the risk of a traffic crash for patients diagnosed with alcoholism [ 15 ]. A direct incentive for physicians’ warnings against drunk driving could be considered to address the problem in the US (the fee in Canada is C$36.25). Physicians need to first realize, of course, that an average drunk driver has a 5%–15% lifetime risk of dying in a traffic crash, physician warnings lead to a one-third relative reduction in the subsequent risk of a serious traffic crash, and most adults who drink and drive visit a physician in the year before dying [ 16 ]. The epidemic of drunk driving needs to be addressed in the US, and 17 March 2017 is a time for physicians to think more about clinical action against drunk driving.

Acknowledgments

The views expressed are those of the authors and do not necessarily reflect the Ontario Ministry of Health and Long-Term Care.

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• 2. Department of Health and Human Services (US). Centers for Disease Control and Prevention. Deaths: Final Data for 2014: Table 10. Washington, DC: CDC; 2016. http://www.cdc.gov/nchs/data/nvsr/nvsr65/nvsr65_04.pdf
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• 12. Centers for Disease Control and Prevention (CDC). What works: Strategies to reduce or prevent drunk driving. Atlanta:US Department of Health & Human Services; 2016. http://www.cdc.gov/motorvehiclesafety/impaired_driving/strategies.html

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Drinking and Driving Research Paper

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The automobile age brought with it unprecedented prosperity and freedom of movement, but motor vehicles have also caused the deaths and injuries of millions of people. From the beginning the abuse of alcohol has been universally viewed as one of the major causes of vehicular carnage, with severe punishments being deemed the best way of dealing with the self-indulgent reprobates responsible.

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According to the sociologist Joseph Gusfield, noted for his work on alcohol in American society, behind all legislation aimed at curtailing drinking and driving is the image of ‘‘the killer drunk,’’ the morally flawed character who has committed more than an ordinary traffic violation. Unlike the social drinker, who knows his limits and respects the law, the drinking driver is a villain who threatens the lives of the innocent through indulgence in his own pleasure. In this legislation, unlike other kinds of traffic law, it is the behavior itself, the hostile, antisocial menace, which is singled out for special disapproval. From this perspective, the enforcement of drinking-driving legislation is as much a matter of public morality as it is of public convenience and safety (Gusfield).

The specter of the killer drunk is the key image that animates ‘‘the dominant paradigm,’’ to use the term coined by H. Laurence Ross, another American sociologist who has done more than any other scholar to elucidate, from an international perspective, the causes and prevention of drinking and driving (Ross, 1982, 1992). The dominant paradigm understands that there is a safe drinking level for the great mass of responsible drivers, differentiated from the levels regularly achieved by the small minority of reckless ‘‘drunken drivers.’’ The problem, in fact, is not ‘‘drinking and driving’’ at all, but ‘‘drunken driving.’’ The dominance of this paradigm in the United States is one reason why the term drunken driving is used so often there, in contrast to most European nations and Australia, where ‘‘drinking and driving’’ or ‘‘drink-driving’’ are the more popular terms.

How one defines the problem is fundamentally important in determining how one thinks about responses. The dominant paradigm calls for severe punishments administered through the criminal justice system. Not only are such punishments fitting, they are capable of deterring further offending, especially if they are backed by rigorous police enforcement. To the extent that the problem is construed in terms of the pathetic drunk rather than the cold-blooded killer, proponents of the dominant paradigm are also comfortable with offering treatment to offenders, provided such programs are not used to evade punishment.

Another way of viewing the problem is through what Laurence Ross calls ‘‘the challenging paradigm.’’ Those who think within this framework are uncomfortable about drawing a rigid line between dangerous drunks and social drinkers, although they recognize that heavy drinkers are a critical part of the problem. Their inspiration is the public health perspective, which is not primarily concerned with righting the moral balance of the world but with minimizing alcohol-related harms. Adherents of the challenging paradigm view alcohol-related accidents as the product of the conjunction of the social institutions of transportation and recreation, rather than as a manifestation of moral dereliction. All developed societies rely, to an increasing extent, on private vehicles for all daily functions including recreation, while the consumption of alcohol is accorded an honored place in afterwork camaraderie, weekend leisure, and business lunches. Large taverns with even larger car parks are built in the suburbs, and drinking to intoxication remains a core recreational activity for large numbers of people.

If the problem is institutions, perhaps the solutions lie in modifying the way these institutions operate. The challenging paradigm has a place for the criminal justice system, especially if the emphasis is on the general deterrence of the whole driving population. However, they also look beyond the criminal justice system to alcohol and transportation policy, exploring the utility of such measures as reducing alcohol availability or making vehicles or roadside hazards more ‘‘forgiving’’ of the errors of the drinking driver.

In the remainder of this discussion we explore many of the issues raised by the dominant and challenging paradigms, and assess the scientific evidence for the claims made.

The Role of Alcohol in Road Accidents

Around the middle of the twentieth century the technical means became available to measure the quantity of alcohol in a person’s blood (the blood alcohol concentration, or BAC, usually measured in terms of grams of alcohol per milliliter of blood). Laboratory research using this technology showed that at BAC levels much lower than those normally associated with intoxication, tasks related to driving performance (such as divided attention tasks) were noticeably affected. Although the effects of BAC depend on such factors as an individual’s weight, rate of drinking, and presence of food in the stomach, deterioration in performance becomes quite marked between BACs of .05 and .08. As a guide, the average man would attain a BAC of .05 or higher if he drank three ‘‘standard drinks’’ (e.g., three mid-size glasses of mid-strength beer) within one hour, without eating.

The alcohol-crash link was confirmed in a series of case-control studies that compared the BACs of drivers experiencing crashes with those of matched non-crash-involved drivers. These studies found that relative crash risks increase exponentially with BAC: at .05 the risk is double that for a zero-BAC driver, at .08 the risk is multiplied by ten, while at .15 or higher (the levels typically attained by drivers arrested for drinking and driving) the relative risk is in the hundreds. The curve is even steeper for serious and fatal crashes, for single-vehicle crashes, and for young people.

While it is likely that factors other than alcohol, such as a propensity to take risks, contribute both to the levels of drinking and to crash involvement, there is a near universal consensus that there is a direct and causal link between alcohol consumption and crashes, especially serious crashes. For example, eliminating alcohol would probably have prevented about 47 percent of fatal crashes in the United States in 1987 (Evans).

Prevalence and Patterns of Drinking and Driving

The most direct way of measuring the prevalence of drinking and driving is to take breath tests from a random sample of motorists. A number of countries carry out these surveys periodically, usually at nights and at weekends when drinking drivers are more numerous. Two groups of nations emerge in these studies. One group includes Scandinavia and Australia, where there are relatively few drinking drivers on the roads. Moderate to high BACs are found among less than 1 percent of drivers in these countries, even at peak leisure times. The second group includes the United States, Canada, France, and the Netherlands, where between 5 and 10 percent of drivers during nighttime leisure hours have moderate to high BACs. These patterns are broadly consistent with overall road fatality rates for different countries, and also with analyses of the BACs of drivers killed. However, in these latter studies even the Scandinavian countries have found that more than a quarter of drivers have positive BACs, despite the low numbers overall of drinking drivers on the road.

A second main way of estimating the prevalence of drinking and driving is to ask random samples of drivers about their behaviors in the recent past. For example, a 1988 study comparing Norwegian, Australian, and American drivers found that 28 percent of Australians, 24 percent of Americans, but only 2 percent of Norwegians admitted to driving in the past year after four or more drinks (Berger et al.). Despite their poor behaviors, 78 percent of the Australians agreed that it was morally wrong to drive after so many drinks, a higher figure than in the United States, but (again) lower than for the Norwegians, who scored a very high 98 percent. Overall, ‘‘general prevention,’’ defined as the influence of moral inhibitions and of social pressures, had taken greater hold in Norway than in the English-speaking countries, but general deterrence (behavior change in response to fear of the threat of legal sanctions) was a more potent force in Australia than in the other countries.

Using intoxication among drivers in fatal crashes as an indicator, dramatic reductions in drinking and driving were experienced in most developed countries in the 1980s. However, the indicators reversed direction in the early 1990s, but then continued in modest decline in the second half of the decade. Formal and informal controls on drinking and driving differ markedly from country to country, but nevertheless there appear to be some common influences. Levels of police enforcement (not the severity of penalties) stand out in all countries as an influence, together with a reduction in per capita alcohol consumption. Attention paid to the problem by political leaders, and the visibility of drinking and driving in the press, appear to be critical factors.

The deterrence of drinking and driving depends primarily on increasing the perceived probability of apprehension in the target population. One way of accomplishing this is to introduce laws that replace the vague offense of ‘‘driving under the influence’’ with the offence of driving with a BAC above a prescribed level (usually .08 or .05). Another way is to initiate a police crackdown on drinking and driving for a period of time. The experience of the United Kingdom in 1967, when it introduced for the first time a .08 BAC limit, illustrates well the usual impact of such interventions. The law was extremely controversial at the time, with the result that most drivers were aware of it and believed they would be caught if they drove after drinking. There was a marked decline in serious accidents at nights and weekends, but not at times when drinking and driving would not be expected. However, the deterrent impact wore off within a few years as drivers gradually became used to the new law, and realized that their chances of detection were in fact not very high.

This pattern of a sharp decline in drinking and driving coincident with a new law or with intensified police enforcement, followed by a gradual decline to pre-intervention levels, is commonly found. Deterrence is an unstable psychological process dependent on continuous publicity and on the perception of a credible police threat. However, random breath testing (RBT) is a major exception to the rule that enforcement effects are invariably temporary.

Under RBT as it is practiced in Australia and some Scandinavian countries, large numbers of motorists are pulled over at random by police and required to take a preliminary breath test, even if they are in no way suspected of having committed an offense or been involved in an accident. Thus RBT should be sharply distinguished from the U.S. practice of sobriety checkpoints, in which police must have reasonable suspicion of alcohol consumption before they can require a test. The RBT law has been very extensively advertised and vigorously enforced in Australia, with the result that 82 percent of motorists reported in 1999 having been stopped at some time (compared with 16 percent in the United Kingdom and 29 percent in the United States).

Time series analyses of accidents show that in Australia RBT had an immediate, substantial, and permanent impact, with every extra one thousand tests conducted each day by police resulting in a 6 percent decline in daily serious accidents (Henstridge et al.). The direct deterrent impact was enhanced by the fact that RBT gave heavy drinkers a legitimate excuse to drink less when drinking with friends. This is a good example of how formal sanctions can reinforce informal sanctions.

The same time series analyses show that a reduction in the legal BAC in some states from .08 to .05 resulted in an average 10 percent decline in serious accidents. This is consistent with experience in other countries where the BAC level has been reduced.

RBT and lower BAC levels concern certainty of detection. Administrative license revocation, the practice in some U.S. states where drivers who drink have their licenses revoked almost as soon as they fail a breath test, concerns swiftness of punishment. Research supports the potential of this procedure to reduce the recidivism of sanctioned drivers and to deter others. As a general rule, the only sanction applied to drivers who drink that reduces recidivism is loss of license. Although many drivers continue to drive while unlicensed, they tend to be more cautious and hence safer. Thus it seems that license loss has (to some extent) a physically incapacitating effect.

Other Countermeasures

License loss is effective for both alcoholrelated and non-alcohol-related accidents, but its impact on drinking and driving can be enhanced if combined with alcohol treatment. While treatment without license suspension is generally ineffective, suspension plus education, psychotherapy counseling, or follow-up contact probation (preferably in combination) produce an additional 7 to 9 percent reduction in recidivism and accidents (Wells-Parker et al.). Ignition interlock devices, which prevent a vehicle being started until the driver passes a breath test, have been shown to be very effective for many highrisk offenders. However, the effects tend to be limited to the period of the court order unless combined with treatment within a case management framework to deal with the underlying problems.

The problem with all countermeasures focused on apprehended offenders is that most serious alcohol-related crashes involve drivers with no prior drinking and driving convictions. Hardcore drivers who drink comprise about 1 percent of drivers on the road, but more than a quarter of drivers killed. Many of these drivers have a history of violence and serious antisocial behavior including crime, with alcohol abuse simply one facet of their deviant careers. It is likely that for this group a radically different approach is needed, involving early childhood interventions (Farrington).

Most accidents do not involve hard-core offenders, and there is therefore a continuing need for countermeasures directed at the general population. Promising measures include promotion of responsible beverage service for bar staff and managers of on-premise alcohol outlets combined with deterrence of drinking and driving through local enforcement; reduction in retail availability of alcohol to minors; and reductions in the number and density of alcohol outlets to limit general access to alcohol. Any measure that reduces per capita alcohol consumption, such as increases in price through taxation, will reduce alcohol-related accidents.

Reducing dependence on driving has similar promise. Successful measures include designated driver programs (someone in a group stays sober so that that person can drive home), safe rides programs, and increasing the age of driver licensing or restricting licenses to daytime use for young drivers. Promoting public transport would certainly be effective if it were ever evaluated for its impact on drinking and driving. Contrary to expectations, there is no evidence that driver education for young people reduces crash involvement. Indeed, the evidence suggests the reverse: by encouraging young people to gain their license at an earlier age, such training increases exposure to risk, and hence accidents.

Finally, making the vehicle and roadside environment more forgiving of the errors of drinking drivers will reduce deaths and injuries. Frangible poles that minimize damage to vehicles; improved response times and skills of emergency medical teams; more use of seatbelts and airbags; and brighter reflective road signs (so impaired drivers notice them) are but a few examples of effective environmental interventions.

Overall, the picture is one of steady progress, with some setbacks. The challenging paradigm, based on the principles of population health, continues to score successes through such strategies as reducing the legal blood alcohol concentration. General deterrence, especially utilizing random enforcement methods, has achieved permanent reductions in alcohol-related crashes, as has administrative license revocation. Treatment combined with license suspension and ignition interlocks reduce recidivism and accidents. Tougher penalties, the major emphasis of the dominant paradigm, show no promise at all.

The challenges include maintaining the deterrent impact of random enforcement; finding long-term ways of dealing with hard-core offenders; optimizing the use of alcohol and driving controls in politically acceptable ways; and maintaining political and media interest in the drinking and driving problem in the face of stiff competition from other social issues. The fact that drinking and driving declined in most countries in the latter part of the twentieth century, despite wide variations in prevention strategies, suggests that within the challenging paradigm there are many pathways to a safer motoring environment.

Bibliography:

• BERGER, DALE; SNORTUM, JOHN R.; HOMEL, ROSS J.; HAUGE, RAGNAR; and LOXLEY, WENDY. ‘‘Deterrence and Prevention of AlcoholImpaired Driving in Australia, the United States and Norway.’’ Justice Quarterly 7 (3) (1990): 453–465.
• EVANS, LEONARD. Traffic Safety and the Driver. New York: Van Nostrand Reinhold, 1991.
• FARRINGTON, DAVID. ‘‘Early Developmental Prevention of Juvenile Delinquency.’’ Criminal Behavior and Mental Health 4 (3) (1994): 209–227.
• GUSFIELD, JOSEPH The Culture of Public Problems: Drinking-Driving and the Symbolic Order. Chicago, Ill.: The University of Chicago Press, 1981.
• HAUGE, R., ed. Scandinavian Studies in Criminology. 6, Drinking and Driving in Scandinavia. Oslo, Norway: Universitetsforlaget, 1978.
• HENSTRIDGE, JOHN; HOMEL, ROSS; and MACKAY, PETA. The Long-Term Effects of Random Breath Testing in Four Australian States: A Time Series Analysis. Canberra, Australia: Federal Office of Road Safety, 1997.
• HINGSON, RALPH. ‘‘Prevention of Drinking and Driving.’’ Alcohol Health and Research World 20, no. 4 (1996): 219–226.
• HOMEL, ROSS. Policing and Punishing the Drinking Driver: A Study of General and Specific Deterrence. New York: Springer-Verlag, 1988.
• HOMEL, ROSS. ‘‘Drivers Who Drink and Rational Choice: Random Breath Testing and the Process of Deterrence.’’ In Routine Activity and Rational Choice. Edited by Ronald V. Clarke and Marcus Felson. Vol. 5 Advances in Criminological Theory. New Brunswick, N.J.: Transaction Publishers, 1993. Pages 59–84.
• LAURENCE, MICHAEL; SNORTUM, JOHN R.; and ZIMRING, FRANKLIN E., eds. Social Control of the Drinking Driver. Chicago, Ill.: The University of Chicago Press, 1988.
• MCCORD, JOAN. ‘‘Drunken Drivers in Longitudinal Perspective.’’ Journal of Studies on Alcohol 45 (1984): 316–320.
• ROSS, H. LAURENCE. Deterring the Drinking Driver: Legal Policy and Social Control. and updated ed. Lexington, Mass.: Lexington Books, 1984.
• ROSS, H. LAURENCE. Confronting Drunk Driving: Social Policy for Saving Lives. New Haven, Conn.: Yale University Press, 1992.
• ROSS, H. LAURENCE. ‘‘Prevalence of Alcohol-Impaired Driving: An International Comparison.’’ Accident Analysis and Prevention 25(6) (1993): 777–779.
• WELLS-PARKER, ELIZABETH; BANGERT-DROWNS, ROBERT; MCMILLEN, ROBERT; and WILLIAMS, MARSHA. ‘‘Research Report: Final Results From a Meta-Analysis of Remedial Interventions with Drink/Drive Offenders.’’ Addiction 90 (7) (1995) 907–926.
• WILSON, R. JEAN, and MANN, ROBERT, eds. Drinking and Driving: Advances in Research and Prevention. New York: The Guilford Press, 1990.

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The Effectiveness of Drinking and Driving Policies for Different Alcohol-Related Fatalities: A Quantile Regression Analysis

Yung-hsiang ying.

1 College of Management, National Taiwan Normal University, Taipei, 106, Taiwan; E-Mail: wt.ude.untn@gniyy

Chin-Chih Wu

2 Institute of China and Asia-Pacific Studies, National Sun Yat-sen University, 70 Lienhai Rd., Kaohsiung 804, Taiwan

Koyin Chang

3 Department of Healthcare Information and Management, Ming Chuan University, 250 Chung-Shan N. Rd., Taipei 111, Taiwan

To understand the impact of drinking and driving laws on drinking and driving fatality rates, this study explored the different effects these laws have on areas with varying severity rates for drinking and driving. Unlike previous studies, this study employed quantile regression analysis. Empirical results showed that policies based on local conditions must be used to effectively reduce drinking and driving fatality rates; that is, different measures should be adopted to target the specific conditions in various regions. For areas with low fatality rates (low quantiles), people’s habits and attitudes toward alcohol should be emphasized instead of transportation safety laws because “preemptive regulations” are more effective. For areas with high fatality rates (or high quantiles), “ ex-post regulations” are more effective, and impact these areas approximately 0.01% to 0.05% more than they do areas with low fatality rates.

1. Introduction

Driving under the influence of alcohol has long been a severe social problem in the United States. In 2009, a study by the National Highway Traffic Safety Administration (NHTSA) indicated that approximately 30 people died in alcohol-related collisions per day (approximately 11,000 deaths per year); that is, one person dies in an alcohol-related collision every 48 min. Additionally, this horrifying figure was the result of already improved traffic safety conditions (the data provided by the NHTSA showed that in approximately 1982, nearly 30,000 people died in alcohol-related collisions in the U.S. per year, which accounted for 60% of the overall traffic crashes. Today that percentage has dropped to 38%). In 1980, Mothers Against Drunk Driving (MADD) was founded in the U.S., dedicating itself to urging state and federal governments to enact a series of drinking and driving policies that significantly reduced alcohol-related fatalities in the U.S. Since then, government officials and scholars have conducted numerous investigations and studies on the effectiveness of drinking and driving policies in reducing alcohol-related fatalities.

The data used in the studies on drunk driving consist of three categories: Cross-sectional data (e.g., Beck et al ., [ 1 ]; Paschall, [ 2 ]; Phelps, [ 3 ]), time-series data (e.g., Whetten-Goldstein et al ., [ 4 ]; Villaveces et al ., [ 5 ]), and panel data (e.g., Chang et al ., [ 6 ]; Lovenheim and Slemrod, [ 7 ]; Hingson et al ., [ 8 ]; Ruhm, [ 9 ]; Males, [ 10 ]; Cook & Tauchen, [ 11 ]; Saffer and Grossman, [ 12 ]). Two estimation methods were used in these traditional econometric studies: (1) the ordinary least square (OLS) method that estimates the conditional mean function of dependent variables; and (2) the least absolute deviation (LAD) method that estimates the conditional median function of dependent variables. These two estimation methods emphasize the central tendency distribution of dependent variables and they both address the data at a macro or comprehensive level instead of examining individual quantiles. However, an observation of the alcohol-related fatality data show that we must study the development tendency of the alcohol-related fatalities of individual quantiles in addition to the central tendency development of alcohol-related fatalities. The reasons are as follows:

1.1. High Consistency of the U.S. Alcohol-Related Fatalities

Figure 1 shows that although U.S. alcohol-related fatalities have declined significantly, the states with high rates of alcohol-related fatalities in 1982 had maintained comparatively high levels in 2009 (e.g., CA, TX, and FL); the opposite situation was also true (e.g., in UT, VT, and RI). Based on this phenomenon, we suspect that drinking and driving policies that showed mean effectiveness had different effects for varying quantiles or alcohol-related fatality rates, preventing the values for states in Quadrant 3 ( i.e. , the states that maintained high rates of alcohol-related fatalities) from moving toward Quadrant 1 ( i.e. , the states that had shown high alcohol-related fatalities transformed into states with low alcohol-related fatalities).

Alcohol-related fatalities in 2009 (horizontal axis) and 1982 (vertical axis).

1.2. Regional Difference in United States Alcohol-Related Fatalities

Figure 2 shows that the states with relatively high alcohol-related fatalities are situated in the west and the south, whereas the states with relatively low alcohol-related fatalities are situated in the northeast, indicating that U.S. alcohol-related fatality are regional. Chang et al . [ 6 ] indicated that the drinking and driving policies in different regions had varying effects (In Chang et al . [ 6 ], the U.S. was divided into Far West, Great Lakes, Mid East, New England, Plains, Rocky Mts., Southeast, and Southwest). We concluded that different drinking and driving policies had different effects depending on the level of alcohol-related fatalities.

Fatalities as a percentage of total fatalities in crashes involving at least one driver with a BAC=0.08+, 2006 (Source: NHTSA).

This finding provided strong motivation to examine the effectiveness of drinking and driving policies under different alcohol-related fatality rates. To effectively discuss the effects of various drinking and driving policies on alcohol-related fatalities in different quantiles, we used the quantile regression method proposed by Koenker and Bassett [ 13 ] for estimation. The simple concept of the advantage of quantile regression, relative to the ordinary least squares regression, is that the quantile regression estimates are more robust against outliers in the response measurements [ 14 ]. Other advantages of the quantile regression method include that it makes no distribution assumptions on the population; it supplements the insufficiency of the traditional regression methods, which focuses only on the mean value of alcohol-related fatalities to estimate and interpret drinking and driving policy parameters; and finally, in our study, it specifically identifies the differing effect levels of drinking and driving policies on alcohol-related fatalities in different quantiles. Thus, we believe that applying QR model has advantages over the traditional method.

The structure of this study is as follows: research motivation and objectives are introduced in Section 1 . In Section 2 , we provide background information for the alcohol control- and road safety-related policies and review empirical studies on drunk driving. In Section 3 , we explain the methodology and the quantile regression model. In Section 4 , we provide the results of the empirical study; we follow the steps provided in the research methodology to implement empirical research and introduce, interpret, and analyze the results from the conducted empirical study. Finally, in Section 5 , we present a conclusion in which the empirical results are integrated and conclusions and suggestions for future studies are provided.

2. Background and Literature Review

The establishment of MADD was significant in the history of U.S. drinking and driving policies. Although the first U.S. law against drunk driving was passed in New York in 1910, other state governments and the federal governments did not pass such laws until MADD was founded in 1980, when the organization launched a wave of lobbying and campaigns. This led to a gradual trend toward more complete U.S. drinking and driving laws and policies.

2.1. Minimum Legal Drinking Age

In 1933, after the U.S. prohibition of the manufacture and sale of alcoholic beverages was lifted, the states began to set minimum legal drinking ages (MLDA), most of which were 21 years of age. By the early 1970s, most states had lowered their MLDAs to between 18 and 20 years of age, resulting in numerous discussions and studies. Most of these studies showed that the rise and decline of teenage car crash fatalities were related to MLDA [ 15 ]. Therefore, in 1984, the U.S. Congress enacted legislation that set the MLDA, stipulating that states that failed to raise their MLDA to 21 would lose a portion of their federal highway construction funding. By 1988, all states had raised their MLDA to 21. MLDA has remained one of the most researched alcohol prevention policies. The studies by Saffer and Grossman [ 12 ], Wilkinson [ 16 ], Wagenaar [ 17 ], Dee [ 18 ], Voas et al . [ 19 ] and Fell et al . [ 20 ] indicated that raised MLDAs effectively reduced alcohol-related traffic collisions.

2.2. Blood Alcohol Concentration

In 1939, the State of Indiana first enacted a blood alcohol concentration (BAC) limit of less than 0.15. In 1983, Oregon and Utah lowered their BAC from 0.1 to 0.08. In a report to Congress in 1991, the NHTSA proposed lowering the BAC to 0.08, and the law limiting BAC was passed by Congress in the same year. In 1998, Congress established the National Mobile Incentive Grant Scheme to strictly enforce the BAC. In 2000, Congress encouraged states to implement BAC restrictions, stipulating that the states that failed to lower their BAC to 0.08 would lose a portion of their federal highway construction funding. By 2004, all states enacted a BAC limit of 0.08.

Hingson et al . [ 21 ], Fell and Voas [ 22 ], Tippetts et al . [ 23 ], Kaplan and Prato [ 24 ], and Wagenaar et al . [ 25 ] showed that lowering the BAC from 0.10 to 0.08 reduced alcohol-related fatalities by 5% to 16%, saving approximately 400 lives per year.

2.3. Zero Tolerance

Zero tolerance was a combination of MLDA and BAC. This act stipulated that drivers under the age of 21 should not demonstrate a BAC exceeding 0.02%. Maryland first passed the Zero Tolerance Law in 1990. In 1995, to encourage other states to enact the Zero Tolerance Law, Congress stipulated under the National Highway Systems Designation Act (NHSDA) that the states that failed to enact the Zero Tolerance Law would lose a portion of their federal highway construction funding. By 1998, all states had implemented the Zero Tolerance Law. Zwerling and Jones [ 26 ], Wagenaar et al . [ 27 ], Voas et al . [ 28 ], Carpenter et al . [ 29 ], and Liang and Huang [ 30 ] showed that the Zero Tolerance Act reduced alcohol-related fatalities by 4% to 24%.

2.4. Open Container Laws

The Open Container Laws regarding drinking and driving stipulated that the drivers would be fined if open containers of alcoholic beverages were found in the cabins of their vehicles. Because this was an interstate law instead of a federal law, the states had the right to decide whether they issued the law and they could also adjust the contents of this law. In 1988, to encourage states to pass the Open Contain Laws, Congress stipulated that states that failed to implement the Open Container Laws would lose a portion of their federal highway construction funding. Currently, only 43 states have enacted this law. Eisenberg [ 31 ] and Benson et al . [ 3 ] showed that this law had a negative correlation with alcohol-related fatalities.

2.5. Driving Under the Influence

The Driving Under the Influence (DUI) Law was constructed in the framework of BAC limits. The “Administrative License Revocation” (ALR) and DUI fine were articles of the DUI Law. Under the ALR law, licenses are immediately revoked whenever a driver either: (1) refuses to submit to BAC testing; or (2) submits to testing with results indicating a BAC over the legal limit of 0.08% (by 2011, 42 states had implemented the ALR, leaving eight states not yet adopting the law: Kentucky, Michigan, Montana, New Jersey, Pennsylvania, Rhode Island, South Dakota, and Tennessee). Ruhm [ 9 ], Voas et al . [ 32 ], and Wagenaar and Maldonado-Molina [ 33 ] indicated that ALR had significant effects on reducing alcohol-related fatalities. The DUI fines varied among states, with the lowest fines for first-time offenders ranging from US$150 in Wisconsin to US$2,000 in Texas. The results of studies on DUI fines differed. Chaloupka et al . [ 34 ] and Wagenaar et al . [ 25 ] indicated that DUI fins had significant effects on reducing alcohol-related fatalities, whereas Sloan et al. [ 35 ] sowed that DUI fines had no significant effects on reducing alcohol-related fatalities, and Young and Likens [ 36 ] found a positive correlation between DUI fines and alcohol-related fatalities.

Based on the definition proposed by Becker and Posner [ 37 ], we classifed these drinking and driving policies into two categories: preventive and ex-postregulations. Preventive regulations were enacted to prevent drinking and driving, including the Beer tax, MLDA, and Open Container Laws, whereas ex-post regulations were enacted to penalize drivers under the influence of alcohol, including the 0.08 BAC limit, ALR, the Safety Belt Law, the Zero Tolerance Law, speed limits, and DUI fines. Although some laws such as the beer tax, speed limits, and the Safety Belt Law were not intended to reduce alcohol-related collisions, numerous studies have observed that these laws had direct and significant effects on alcohol-related fatalities. Specifically, the effects of the beer tax on alcohol-related fatalities were widely examined. For example, the empirical results of Chaloupka and Wechsler [ 38 ], Phelps [ 39 ], Kenkel [ 40 ], Saffer and Grossman [ 12 ], and Mann et al . [ 41 ] showed a significant negative correlation between the beer tax and alcohol-related fatalities, whereas the empirical results of Sloan and Githens [ 42 ], Dee [ 18 ], Mast et al . [ 43 ], and Young and Likens [ 36 ] indicated that the relationship between the beer tax and alcohol-related fatalities was neither significant nor necessarily negatively correlated.

3. Research Model and Methodology

3.1. panel data quantile regression model.

The quantile regression (QR) analysis was proposed in Koenker and Bassett [ 13 ] as an expansion of the least absolute deviation (LAD). QR can be used to detail the performance of explanatory variables under the influence of conditional medians. Additionally, it can be expanded to analyze the performance of variables under the influence of different conditional quantiles.

Based on the descriptions in the study by Koenker & Bassett (1978), we established a random variable cumulative distribution function, as shown in Equation (1):

where y it represents the dependent explanatory variable vector, and x it is the independent explanatory variable vector. β is the regression coefficient vector obtained through estimation satisfying (1) and varies according to different quantiles τ . Therefore, β ( τ ) represents the regression coefficient vector under the influence of the τth quartile.

where ε it ( τ ) represents the random error under quantile τ , and α i represents the regional fixed effects that are unaffected by quantile ( τ ) and capture unobserved time-invariant heterogeneity between regions [ 14 ]. Also included is the state-specific time fixed effect to guarantee that the results are not due to the trend of fatalities caused by drunk driving [ 9 ]. The conditional expectation value in traditional panel data analysis is a linear operator; thus, within group estimation is used to eliminate the α i in the model and prevent biased estimation. However, the conditional quantile in the QR analysis is not a linear estimator, and within group estimation cannot be used to eliminate the fixed effects. Therefore, Koenker [ 14 ] introduced an objective function with penalty terms to eliminate the fixed effects, as shown in Equation (2):

Based on the suggestions in Lamarche [ 44 ], we used the bootstrap method for sampling estimation. In this method, the re-sampling of samples was used to simulate the population distribution. We also relaxed the assumption limit that requires the conditional distribution of the errors to be homoscedastic [ 45 ]. Therefore, a variance matrix estimation equation with consistency was obtained, as shown in Equation (3).

The QR model can describe the performances of different quantile conditional distributions and therefore can more fully describe the characteristics of samples. This is different from the OLS model describes only the mean marginal effects of the explanatory variables on the explained variables.

3.2. Empirical Model

Because this model was comparatively suitable, we used the panel data QR model to explore and verify whether changes in the effectiveness of drinking and driving policies occur with varying levels of alcohol-related fatalities. Based on the framework in Koenker [ 14 ], we established an empirical model for panel data QR, as shown in Equation (4):

where ε it ( τ )and α i are explained in the paragraph following Equation (1.1). Annual data from the 48 contiguous states for the years 1982 to 2009 are employed. ARFR it represents the alcohol-related fatalities per 100,000 population (according to Chang et al . [ 6 ], a lowered ARFR indicates that the traffic conditions in a state were undergoing improvement, that is, improved traffic conditions were beneficial to reducing alcohol-related fatalities) obtained from the Fatal Accident Reporting System (FARS) of the NHTSA. CONTROL it represents geo-economic factors, such as population density (Pop. density it ), income (Income it ), unemployment rates (Unemp. rate it ), teenage/young driver ratio (Under24 it ), and U.S. administrative districts. L it represents the nine drinking and driving policies selected for this discussion: The beer tax (Beer tax it ), MLDA (MLDA it ), BAC (Bac08 it ), ALR (ALR it ), the Safety Belt Law (Belt it ), the Zero Tolerance Law (Zero tolerance it ), Open Container Laws (Open container it ), the speed limit (Speed limit it ), and DUI fines (DUI fine it ) (these policies have been passed and implemented in all states at different times). These policies were set as the dummy variables in this model except for the beer tax. If states had adopted a policy, it was marked as 1; if they had not, it was marked as 0. Please refer to Table 1 for the details of the variables.

Variable definition and statistics.

Note: 1. NHTSA represents National Highway Traffic Safety Administration; 2. The abbreviation of each state in the USA are explained in Table A1 .

Table 2 shows four characteristics: (1) In the areas with low rates of alcohol-related fatalities, increases in unemployment rates and the number of young drivers (licensed drivers aged between 16 and 24 years of age) correlated with significant increases in alcohol-related fatalities. In these areas, preventive regulations (such as MLDA and the beer tax) were relatively more effective in reducing alcohol-related fatalities than ex-post regulations; (2) In areas with high rates of alcohol-related fatalities, socio-economic factors such as employment rate, and the number of young drivers had no significant effects on fatalities. In these areas, ex-post regulations (such as BAC limit (0.08) and ALR) correlated with reductions in fatalities at 1% significance level; (3) In terms of regional fixed effect, all coefficients of three regions are negative, indicating that the omitted region, South, had the highest alcohol-related fatalities rate. Since the second highest region was West, our results appear to support the original finding described in Figure 2 ; (4) The effects of preventive regulations declined as the rate of alcohol-related fatalities increased, whereas the opposite was observed for ex-post regulations. This indicates that in areas with high rates of alcohol-related fatalities, ex-post regulations were more effective than preventive regulations. The only ineffective traffic law in reducing alcohol-related fatalities in all quantiles is the speed limit. In the following section, we detail the effectiveness of various drinking and driving policies and other control variables in areas with high, medium, and low rates of alcohol-related fatalities.

Panel data quantile regression analysis.

Notes : 1. The 25, 50, and 75 percentiles represent the areas with 25th, 50th, and 75th percentiles of the rate of alcohol-related fatality; 2. ARFR, Beer tax, income, unemployment rate, and population density are in natural logarithms; 3. The geographic area “South” is omitted for the comparison base; 4. ***, **, * represent significance levels of 1%, 5%, and 10%, respectively; 5. State-specific time dummies were also included in the regressions while their coefficients are not reported to reduce paper length.

4.1. Areas with Low Alcohol-Related Fatalities

Table 2 shows that all drinking and driving polices except for speed limit had significant effects on lowering rates of alcohol-related fatalities in these areas. Among all policies, the beer tax was the most effective in lowering fatalities. Assuming that other conditions remained constant, when the beer tax increased by 1%, the rate of alcohol-related fatalities declined by 0.41%. Additionally, zero tolerance, the Open Container Law, and BAC effectively reduced the rate of fatalities in these areas, showing decreases of 0.18%, 0.14%, and 0.06%, respectively.

Other economic and demographic variables, such as per capita income, unemployment rates, and the number of young drivers all had significant effects in these areas at 5% level. Unemployment rates and the number of young drivers have a significant positive correlation with alcohol-related fatalities, that is, increases in unemployment rates and the proportion of young drivers caused an increase in fatalities. In particular, when the number of young drivers increased 1%, the rate of fatalities increased 0.08% holding other conditions constant. Conversely, per capita income had a significant negative correlation with alcohol-related fatalities. Assuming that other conditions remained constant, when the per capita income increased 1%, the alcohol-related fatalities declined 0.036%.

From these analyses, we observed that in the areas with low alcohol-related fatalities, in addition to the increased fatalities caused by economic pressure from unemployment and low per capita income [ 46 ], the effects of young drivers on increased alcohol-related fatalities should not be overlooked. In summary, in these areas, alcohol abuse and poor attitudes toward alcohol had a more severe effect on alcohol-related fatalities than poor traffic conditions [ 6 ]. Therefore, preventive regulations that are intented to prevent drunk driving were more effective and important than ex-post regulations that are intended to penalize drunk driving offenders.

4.2. Areas with Medium Alcohol-Related Fatalities

In the areas with medium rates of alcohol-related fatalities, the effects of the speed limit were insignificant, that is, the speed limit in these areas failed to effectively reduce rates of alcohol-related fatalities. Other drinking and driving policies had significant effects on the rates of alcohol-related fatalities in these areas. The beer tax was still the most effective in reducing rates of alcohol-related fatalities. Assuming that other conditions remained constant, when the beer tax increased by 1%, fatalities declined by 0.31%. Additionally, the zero tolerance, open container, and BAC regulations in these areas effectively reduced the rates of alcohol-related fatalities, by 0.25%, 0.19%, and 0.07%, respectively.

In these areas, the number of young drivers had no significant effects on alcohol-related fatalities, indicating that young drivers were not the major cause or focus of alcohol-related fatalities in these areas. Other economic and demographic variables (such as per capita income, unemployment rates, and population density) had significant effects on fatalities at 10% significance level. In particular, per capita income and population density had significant negative correlations with alcohol-related fatalities, that is, when per capita income or population density increased, fatalities declined by 0.016% and 0.165%, respectively. Unemployment rates had a significant positive correlation with fatalities at 10% significance level. Assuming that other conditions remained constant, when unemployment rates increased by 1%, fatalities increased by 0.011%.

From these analyses we observed that in the areas with medium alcohol-related fatalities, traffic conditions should be improved and alcohol abuse and poor attitudes toward alcohol should be discouraged to reduce alcohol-related fatalities. In summary, preventive and ex-post regulations were both significant.

4.3. Areas with High Alcohol-Related Fatalities

Most of the included drinking and driving policies all had significant effects in the areas with high alcohol-related fatalities. In particular, the three most effective traffic laws for reducing fatalities were zero tolerance, open container, and BAC for reducing fatalities rates by 0.28%, 0.103%, and 0.101%, respectively. The only traffic law that showed insignificant result was speed limit. In these areas, fewer economic and demographic variables (only per capita income and population density) had significant effects on reducing alcohol-related fatalities, indicating that unemployment rates and the number of young drivers were not major causes of drunk driving in these areas. In summary, improving traffic conditions or creating safe traffic conditions is essential for reducing alcohol-related fatalities in these areas. Additionally, ex-post regulations such as zero tolerance and BAC were relatively more effective than preventive ones.

4.4. Comparisons across Quantiles

To test whether all three quantiles were statistically different from each other, Chow tests were performed and presented in Table 3 , which indicates that the QR results were significantly different at 5% level for each pair of QR comparison. For systemic comparisons between coefficients across quantiles, differences between coefficients for each variable were computed and the results are presented in Table 3 . For the laws that are more effective in the areas with low alcohol-related fatalities, negative numbers appear in the columns of coefficient difference throughout the three pairs of comparison were obtained, which were MLDA and speed limit. Beer tax also worked more effectively in the areas with low alcohol-related fatalities. BAC and zero tolerance, on the other hand, are more effective in the areas with high alcohol-related fatalities. Thus, the areas with different conditions of alcohol-related fatalities should focus on different policies when enforcing the laws. In short, compared with areas that had low fatalities, the effects of preventive regulations for suppressing alcohol-related fatalities had declined in the areas with high fatalities, whereas the effects of ex-post regulations for suppressing fatality rates had increased in general. (The changes of the effects of preventive regulations are as follows: MLDA declined from 0.012% to 0.004%, and the Open Container Law from 0.142% to 0.103%. The changes of the effects of ex-post regulations are as follows: Zero Tolerance increased from 0.184% to 0.283%, BAC (0.08) increased from 0.066% to 0.101%, ALR from 0.058% to 0.065%, and DUI fines from 0.034% to 0.042%.)

Comparison between coefficients from different quantile regressions.

Notes : 1. Each column presents the difference of coefficients between different quantile regressions; 2. ***, **, * represent significance levels of 1%, 5%, and 10%, respectively; 3. The differences in coefficients of state-specific time dummies and constants are not shown.

5. Discussion

It is important for the relevant authorities to gain area-specific understanding of laws when amending them in order to save more lives from drinking and driving. Thus, we used the results from the empirical study on relevant policies to verify the arguments and discourse described above. Comparing the effects of all traffic laws in the three different quantiles, the most effective ones are the same for all three quantiles in the same order—zero tolerance, open container, and BAC. However, some laws are more effective in the areas with high alcohol-related fatalities, some are more effective in the areas with low alcohol-related fatalities, and others may not show consistent patterns across quantiles. In the areas with low alcohol-related fatalities, preventive regulations (beer tax, MLDA, and open container) may be more effective than ex-post regulations (such as BAC and zero tolerance), whereas ex-post regulations were more effective in areas with high fatalities, with an increase in effectiveness of 0.04% to 0.10% compared with their influence in the areas with low fatalities. Beer tax is most effective for the areas with low rate of alcohol fatalities but zero tolerance is most effective for the areas with high alcohol fatalities. DUI fine laws are effective for the areas with high alcohol fatalities but not so effective for the medium and low rates of alcohol fatalities.

These analyses show that the effectiveness of drinking and driving policies differed in areas with different rates of alcohol-related fatalities. Our results of all the policy effectiveness were statistically significant at 10% level or higher, except for DUI and speed limit in the areas with medium or high rates of alcohol-related fatalities. Even though the results were statistically significant in general, they might not imply social significance given the fact that the effectiveness (the magnitude of coefficients) of the laws was small. The law with greatest impact was zero tolerance, which decreased the rate of alcohol-related fatalities by 0.184%, 0.248%, and 0.283% in the areas with low, medium, and high rates of alcohol-related fatalities, respectively (as shown in Table 2 ). However, these figures could be translated to 18.82, 25.36, and 28.94 lives saved, respectively, given that total 10,228 people were killed in alcohol-impaired driving crashes in 2012 (Dept of Transportation 2012). While this study did not intend to address the issue of social significance (To determine whether the results are socially significant, which can be referred to changes on measures that are important to society, some cut off points or thresholds need to be carefully defined [ 47 , 48 ], which is beyond the scope of this study.) and the implementation of each traffic law did not seem to save many lives, it is believed that each life counts and is of great importance to their family. Therefore, it is crucial for the relevant authorities to gain better understanding of traffic laws. When deciding on methods by which to lower alcohol-related fatalities, the U.S. states should consider the characteristics of drunk driving in their areas to effectively reduce fatality rates.

6. Conclusions

The statistics from the FARS of the NHTSA show that approximately 30,000 people were killed or injured in car crashes in the U.S. in 2009. Forty percent of these crashes occurred during weekends (approximately 12,000 casualties), possibly because people consume excessive quantities of alcohol at social engagements on weekends, causing severe alcohol-related crashes [ 37 ]. This indicates that drunk driving remains a severe social problem in the U.S. that motivates scholars and experts to identify factors that can reduce alcohol-related fatalities.

In this study, we used the alcohol-related fatalities per 100,000 people in the U.S. states between 1980 and 2009 for our analysis. The data show the following phenomena: (1) consistency: areas with high rates of alcohol-related fatalities in the 1980s remained so in 2009; and (2) regionality: areas with higher rates of alcohol-related fatalities were situated in the west and south, whereas areas with lower alcohol-related fatalities were situated in the northeast. These characteristics led us to question if drinking and driving policies had the same effects in areas with different rates of alcohol-related fatalities. Therefore, we used the QR method to discuss the effectiveness of various drinking and driving policies for different quantiles of alcohol-related fatalities.

The results from the empirical study show demographic factors such as income, unemployment rates, young driver ratio, and population density were all significant in areas with low rates of alcohol-related fatalities; while only income and population density were significant in areas with high rates of alcohol-related fatalities. Considering the numbers of coefficients, we also find that lower beer tax and declined economic conditions (such as decreased income or increased unemployment) are correlated with higher rate of alcohol-related fatalities with impact greater in areas with low alcohol-related fatalities than in high fatality areas. Additionally, increased numbers of young drivers in areas with low rates of alcohol-related fatalities result in increased fatalities, whereas they did not significantly affect the fatalities in the areas with higher rates of alcohol-related fatalities. This implies that in areas with low alcohol-related fatalities (as compared to high fatality areas), drinking habits and attitudes may be restrained more easily by stricter drinking and driving policies and these areas are influenced to a greater extent by economic and demographic conditions. On the other hand, drinking habits and attitudes may not be easily changed in the areas with high alcohol-related fatalities; ex-post regulations are thus important for discouraging drinking people driving on the road. As a result, ex-post regulations are more important in the areas with high fatalities whereas preventive regulations are intended to prevent alcohol abuse and thus decrease alcohol-related fatalities in the areas with low fatalities.

Abbreviation of the states in the USA (by alphabetic order).

Conflicts of Interest

The authors declare no conflict of interest.

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30-Year-Old Dies In Fiery Drunk Driving Crash In Hicksville, Police Say

An accused drunk driver is behind bars following a fiery, two-vehicle crash that killed a man on Long Island.

The intersection of Prospect Avenue and Cantiague Rock Road in Hicksville.

The wreck happened in Hicksville, near the intersection of Prospect Avenue and Cantiague Rock Road, at around 11:20 p.m. Saturday, Sept. 21.

Nassau County Police said a Subaru was making a left turn from eastbound Prospect Avenue onto northbound Cantiague Rock Road when it struck a westbound Honda.

The impact caused the Subaru to catch fire, trapping the 30-year-old male driver inside, police said. He was pronounced dead at the scene.

An investigation found that the Honda driver, 42-year-old Miguel Nolasco, of Roosevelt, was intoxicated at the time, police said.

Nolasco was hospitalized with undisclosed injuries. Following his release, he was expected to be arraigned on the following charges:

• Vehicular manslaughter
• Manslaughter
• Driving while intoxicated

The victim had not been publicly identified as of Monday afternoon, Sept. 23. 

Saturday's crash came amid a particularly deadly weekend on Long Island roadways. Two people, including an 18-year-old man, were killed in a West Babylon crash earlier in the day, and a 17-year-old boy died in a single-car wreck in Merrick early Sunday, Sept. 22.

In another incident early Saturday, a 55-year-old man was struck and killed in North Massapequa while crossing Hicksville Road. The driver then fled the scene.

Check back to Daily Voice for updates.

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Milford firefighter accused of driving drunk, crashing into a building indicted

PENOBSCOT COUNTY, Maine (WABI) - A Milford firefighter who was placed on leave after being accused of driving drunk and crashing into a building has been indicted by a Penobscot County Grand Jury.

Wayne Feero, 53, is charged with aggravated criminal mischief, criminal OUI, and driving to endanger.

Authorities say Feero was taken to the hospital with minor injuries after he crashed into a vacant building on Main Street in Milford last May.

No one else was hurt in the crash.

Copyright 2024 WABI. All rights reserved.

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Repeat offending Brit sentenced to 15 weeks in Monaco for drunk driving

Posted by Jack Brodie, Editor-in-Chief | Sep 25, 2024 | Law & Order

A 32-year-old British man, a repeat offender, has been sentenced to 15 weeks in prison for drunk driving in Monaco, Monaco-Matin initially reports. The case, which required two court hearings, highlighted the man’s struggle with alcohol and his dangerous driving habits, as seen through video surveillance on Friday, September 13.

Initially arrested with a blood alcohol level of 0.67 mg/l, the man was immediately tried and sentenced to three weeks in prison, along with an additional three months after the revocation of a suspended sentence from a prior conviction in 2022. His driving license was revoked, and he is banned from applying for a new one for two years. The court also ordered him to undergo mandatory treatment for alcoholism for the next three years.

The court’s decision aimed to address not only the legal consequences of his reckless behavior but also the underlying issues of his addiction. The man attributed his drinking problem to personal struggles, including a strained relationship with a suicidal partner and a lifestyle of idleness.

“You are a danger,” prosecutor Emmanuelle Carniello stated, stressing the need for a stricter sentence. Initially, the prosecution had called for six months of imprisonment and the revocation of the previous suspended sentence.

At the first hearing, the visibly anxious defendant tried to explain his behavior, admitting he had consumed an excessive amount of alcohol due to personal worries. He told the court he drank between 10 to 15 cans of beer in the early morning hours because of his partner’s illness, which had caused him to panic.

Judge Florestan Bellinzona, presiding over the case, postponed the sentencing until a psychiatric evaluation could be completed. At the second hearing, the judge acknowledged the man’s newfound awareness of his alcoholism. However, the prosecutor remained unconvinced, stating, “It’s a failure! The treatment has been ineffective, and there’s no understanding of the dangers of driving after drinking.”

In his defense, the man’s lawyer highlighted his troubled background, including an alcoholic mother and ongoing anxiety issues. The defense argued that an extended prison sentence would be unbearable for someone already dealing with personal and emotional challenges, requesting community service or a split sentence as an alternative.

Ultimately, the court showed leniency but issued a stern warning. The man was sentenced to 15 weeks in prison and instructed to seek further treatment. Judge Bellinzona cautioned that any future offences could result in even harsher penalties, stating, “Be careful with the next incident—you could face an additional six months.”

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Courts | Woman sentenced to 12 years in wrong-way…

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Courts | Woman sentenced to 12 years in wrong-way Downtown Tunnel crash that killed transgender activist

Dominique Chiqura Goodwin , 27, is set to serve 12 years and four months in prison, according to a release from Norfolk’s commonwealth’s attorney.

On Dec. 30, Goodwin drove her 2018 Volkswagen Tiguan in Portsmouth after “a night of drinking,” the release stated. Goodwin turned the wrong way onto Bart Street, a one-way off ramp from Interstate 264. Goodwin continued to drive east, toward Norfolk, inside the westbound side of the tunnel.

About halfway through the tunnel, Goodwin crashed head-on into a 2007 Chevrolet Equinox driven by Shelby Riddick-Walker, 43, who was killed on impact.

Goodwin’s hospital records indicated her blood alcohol content was 0.22, nearly three times the legal driving limit, less than an hour after the crash. Investigators reported finding an empty liquor bottle inside Goodwin’s vehicle.

“Dominique Goodwin made the decision to drink hard and drive. Ms. Goodwin killed Shelby Riddick-Walker, a beloved mentor to many in the trans community,” stated Commonwealth’s Attorney Ramin Fatehi. “Shelby did nothing to deserve her death, and we lost the light of her work too soon.”

Riddick-Walker was a former employee of the LGBT Life Center and a recent employee at 37th and Zen, according to WAVY-TV .

Goodwin agreed to plead guilty to aggravated involuntary vehicular manslaughter. The plea deal included another seven years and eight months suspended on the condition that Goodwin forfeit her driver’s license and complete five years of good behavior and three years of supervised probation after her release.

Hannah Eason, [email protected]

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