Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2025 Oct 1.
Published in final edited form as: Alcohol Clin Exp Res (Hoboken). 2024 Aug 23;48(10):1923–1928. doi: 10.1111/acer.15429

The impact of COVID-19-related national lockdowns on alcohol-related traffic collisions, injuries, and fatalities in Lithuania

Shannon Lange 1,2,3,4,*, Huan Jiang 1,2,5, Laura Miščikienė 6, Alexander Tran 1,2, Mindaugas Štelemėkas 6,7, Jürgen Rehm 1,2,3,4,5,8,9
PMCID: PMC11464170  NIHMSID: NIHMS2016774  PMID: 39179515

Abstract

Background:

The national lockdowns that occurred all over the world in response to the Coronavirus Disease 2019 (COVID-19) pandemic have been found to have impacted alcohol use. The aim was to evaluate the impact of COVID-19-related national lockdowns on alcohol-related traffic collisions, injuries, and fatalities in Lithuania.

Methods:

Using monthly data from the Lithuanian Road Police Service for Jan. 2004 to Dec. 2022, we performed interrupted time-series analyses using a generalized additive model to evaluate the impact of COVID-19-related national lockdowns on alcohol-related traffic collisions, injuries, and fatalities. In Lithuania, the COVID-19-related lockdowns occurred from Mar. 2020 to Jun. 2020, and from Nov. 2020 to Jun. 2021.

Results:

While overall rates for traffic collisions and injuries decreased during the COVID-19-related lockdowns in Lithuania, these lockdowns were associated with a 3.21% (95% CI: 1.19%, 5.23%) increase in the relative proportion of alcohol-related traffic collisions and a 2.46% (95% CI: 0.12%, 4.80%) increase in the relative proportion of alcohol-related traffic injuries. The association between the lockdowns and alcohol-related traffic fatalities was not statistically significant.

Conclusion:

The COVID-19-related national lockdowns in Lithuania were associated with a decrease in the overall rate of traffic collisions and injuries, but an increase in the relative proportion of alcohol-related traffic collisions and injuries.

Keywords: Alcohol-related, COVID-19, Time-series analysis, Traffic harm

Graphical Abstract

graphic file with name nihms-2016774-f0001.jpg

The COVID-19-related national lockdowns that occurred all over the world in response to the pandemic have been found to have impacted alcohol use. Using monthly data, interrupted time-series analyses were performed to evaluate the impact of COVID-19-related lockdowns on alcohol-related traffic collisions, injuries, and fatalities in Lithuania. The lockdowns were found to be associated with a decrease in the overall rate of traffic collisions and injuries, but a significant increase in the relative proportion of alcohol-related traffic collisions and injuries.

Introduction

During the Coronavirus Disease 2019 (COVID-19) pandemic, countries all over the world implemented various public health measures (e.g., complete lockdowns, occupancy limitations, and travel restrictions) in an effort to limit the spread of the virus; Lithuania was no exception. In Lithuania, two national lockdowns occurred: the first from Mar. 16, 2020 to Jun. 16, 2020, and the second from Nov. 7, 2020 to Jun. 30, 2021. Based on the international literature, it has been found that such measures inherently impacted alcohol use (Acuff et al., 2022, González-Monroy et al., 2021, Kilian et al., 2022, Roberts et al., 2021, Schmidt et al., 2021, Sohi et al., 2022). While in many countries, alcohol use decreased overall (World Health Organization In press), for subpopulations that experienced more stress, consumption increased, especially for those who had already been drinking heavily pre-pandemic (Kilian et al., 2022, Roberts et al., 2021, Schmidt et al., 2021, Sohi et al., 2022) –a phenomenon commonly referred to as the polarization of drinking. In Lithuania, it was found that although 85.8% of participants surveyed either consumed the same amount or decreased their alcohol consumption during the COIVD-19-related lockdowns, compared to before, 14.2% reportedly increased their consumption (Kriaucioniene et al., 2020). In a more recent study comparing data from the Reducing Alcohol Related Harm Standard European Alcohol Survey 2015 to 2020, when average daily consumption (in grams per day) was decomposed into deciles, it was found that consumption decreased for all deciles except for the upper decile, which significantly increased (p<0.001, M2015(SD)=230.9(43.0) g/day and M2020(SD)=309.6(169.8) g/day) (Tran et al., 2024). Further, the average daily consumption in 2020 for the upper decile was near parity for males and females (p=0.79, Mmales(SD)= 310.4(171.7) g/day and Mfemale(SD)= 307.4(168.9) g/day). Taken together, the evidence suggests that not only did a polarization of drinking occur in Lithuania, but the gap between the sexes for average daily consumption among the heaviest consumers closed in 2020.

Injuries, for instance traffic injuries, have been shown to be predominantly related to heavy alcohol use (Cherpitel et al., 2015, Taylor et al., 2010). As such, it is reasonable to postulate that the change in both the level and pattern of drinking that occurred during the COVID-19-related lockdowns would have had an impact on alcohol-related traffic injuries. However, because of the stay-at-home orders it is likely that there would have been less traffic overall, as shown elsewhere (Calderon-Anyosa and Kaufman, 2021).

The aim of the current study was to evaluate the impact of COVID-19-related national lockdowns on alcohol-related traffic collisions, injuries, and fatalities in Lithuania. The following hypotheses were tested, as pre-specified in the grant submission (Rehm J. and Štelemėkas M, 2023) and derived from general reviews on changes of alcohol use during crisis situations (de Goeij et al., 2015, Rehm et al., 2020a): COVID-19-related lockdowns in Lithuania will be associated with a reduction in the rate of traffic collisions, injuries, and fatalities, but due to the suspected polarization of drinking, there will be an increase in the relative proportion of alcohol-related traffic collisions, injuries, and fatalities.

Methods

Measures

Monthly data on traffic collisions (number of incidents), injuries (number of people injured in a traffic collision) and fatalities (number of fatalities resulting from a traffic collision) were obtained from the Lithuanian Road Police Service (Lietuvos kelių policijos tarnyba) for January 2004 to December 2023 (Lietuvos kelių policijos tarnyba [Lithuanian Road Police Service], 2024). According to Lithuanian law, a traffic collision is defined as an incident on the road, in public or private property, during which people are injured, killed, or at least one vehicle, cargo, road, its structures or any other property at the scene is damaged, when a vehicle is in motion (Seimas of the Republic of Lithuania, 2000). As such, one traffic collision can result in one or more injured people or fatalities, but not necessarily. The number of traffic collisions, injuries and fatalities were divided by the monthly population and then multiplied by 100,000, producing monthly rates for each of the three traffic indictors per 100,000 people. For each of the three traffic collision indicators, a subset with alcohol involvement was available. It should be noted that the legal BAC levels changed in 2015 for specific groups of drivers and these changes are reflected in the data analyzed. Alcohol involvement for 2004 to 2014 is defined as the driver of the motor vehicle having a blood alcohol concentration (BAC) or a breathalyzer result indicating a BAC of ≥0.4 per mille (‰; or ≥0.2‰ for novice drivers), while for 2015 to 2024 the legal limit was reduced to 0‰ for novice drivers, as well as professional drivers and motorbike riders.

Statistical analysis

To test our hypotheses, we applied a generalized additive model (GAM) for each outcome (i.e., the rate (per 100,000 population) of traffic collisions, injuries and fatalities, and the relative proportion that were alcohol-related). GAMs were used because of their ability to capture non-linear trends by applying separate smooth functions for applicable predictor variables. Further, in a recently conducted simulation study (Jiang et al., 2024), it was found that GAM is more robust than ARIMA when the model is mis-specified. Each model was adjusted for time as well as highly effective alcohol control interventions that occurred during the time series. The latter involved two periods of intensive alcohol control policy implementation, which have been shown to have been associated with a decrease in alcohol-attributable mortality (Rehm et al., 2024): the first from 2008 to 2009, and the second from 2017 to 2018. In brief, 2008 was declared “the year of sobriety” in Lithuania, and in addition, on Jan. 1, 2008 excise tax increased by 10–20%; and on Jan. 1, 2009 off-premise sales of alcoholic beverages were banned between 10 p.m. and 8 a.m. (Miščikienė et al., 2020, Rehm et al., 2023b). It is important to note that the 2008–09 alcohol policies overlapped with the global financial crisis, which specifically impacted Lithuania from Aug. 2008 to Dec. 2009. On Mar. 1, 2017 excise tax increased by 112% for beer, 111% for wine, 92–94% for intermediate products, and 23% for spirits; and on Jan. 1, 2018 retail hours for off-premise sales further reduced from 10 a.m. until 8 p.m. on Mondays to Saturdays and from 10 a.m. to 3 p.m. on Sundays, in addition to increasing a Minimal Legal Purchasing Age from 18 to 20 years and a near total ban on advertising (Miščikienė et al., 2020, Rehm et al., 2023b). The taxation and availability policies had been shown to have immediate effects on consumption and all-cause mortality lasting at least for 12 months (Rehm et al., 2023a). Furthermore, an earlier study by the investigative team has shown that alcohol control policies can impact alcohol-related traffic indicators (Rehm et al., 2020b).

The rates of traffic collisions, injuries and fatalities were approximately normally distributed after log transformation, allowing for the use of linear models. For all models, Akaike information criterion (AIC) and R-squared values were used to evaluate model fit (Harrell FE Jr., 2015). Seasonality was adjusted for by adding smoothing splines to represent the monthly patterns, defined by thin plate regression splines–selected because their multidimensional appearance makes them ideal for examining the combined effect of two predictors on a single outcome (Wood, 2003). Residuals were examined for autocorrelation using the Box-Jenkins method. Where necessary, ARIMA (p, q) was added with p auto-regressive term and q moving average term using the auto.arima() function (forecast package) in R version 4.2.3 (R Core Team, 2023).

Results

There was a total of 57,356 traffic collisions (1,876 per 100,000 population), 74,198 traffic injuries (2,421 per 100,000 population), and 4,402 traffic fatalities (141 per 100,000 population) in Lithuania between 2004 and 2023. During this time the relative proportion of alcohol-related traffic collisions, injuries and fatalities were 0.12, 0.13, and 0.15, respectively. For a demonstration of the general trends of traffic collision, injury, and fatality rates per 100,000 population and the relative proportion of alcohol-related traffic collisions, injuries and fatalities see Figure 1.

Figure 1:

Figure 1:

Open in a new tab

Rate (per 100,000 population) of traffic collisions, injuries, and fatalities (a) and relative proportion of alcohol-related traffic collisions, injuries and fatalities (b) in Lithuania between 2004 and 2024

Note. The grey shaded areas depict the COVID-19-related national lockdowns: the first from Mar. 2020 to Jun. 2020, and the second from Nov. 2020 to Jun. 2021.

The COVID-19-related lockdowns in Lithuania were associated with a statistically significant reduction in the log-transformed traffic collision and injury rates per 100,000 population. Specifically, the COVID-19-related lockdowns were associated with a 32.02% ((exp(−0.386)-1)*100%; 95% CI: −40.25%, −22.66%) reduction in the rate of traffic collisions and a 35.08% ((exp(−0.432)-1)*100%; 95% CI: −43.28%, −25.70%) reduction in the rate of traffic injuries (Table 1). The association between the lockdowns and the log-transformed traffic fatality rate per 100,000 population was not statistically significant, but was in the hypothesized direction.

Table 1.

Effects of COVD-19-related lockdowns on traffic collision, injury and fatality rates per 100,000 population (log transformed), 2004–2023

Effect estimate SE 95% CI P-value
Lower Upper
Traffic collisions
Intercept 0.377 0.098 0.185 0.569 <0.001
Time 0.011 0.001 0.009 0.013 <0.001
COVID-19 lockdown −0.386 0.066 −0.515 −0.257 <0.001
Alcohol policies/financial crisis, 2008–09 0.340 0.034 0.273 0.407 <0.001
Alcohol policies, 2017–18 −0.037 0.064 −0.162 0.088 0.567
 Adjusted R2 0.721
Traffic injuries
Intercept 0.423 0.101 0.225 0.621 <0.001
Time 0.013 0.001 0.011 0.015 <0.001
COVID-19 lockdown −0.432 0.069 −0.567 −0.297 <0.001
Alcohol policies/financial crisis, 2008–09 0.351 0.035 0.282 0.420 <0.001
Alcohol policies, 2017–18 −0.084 0.067 −0.215 0.047 0.208
 Adjusted R2 0.741
Traffic fatalities
Intercept −0.388 0.176 −0.733 −0.043 0.028
Time −0.005 0.002 −0.009 −0.001 0.002
COVID-19 lockdown −0.060 0.139 −0.332 0.212 0.668
Alcohol policies/financial crisis, 2008–09 0.209 0.072 0.068 0.350 0.004
Alcohol policies, 2017–18 −0.004 0.126 −0.251 0.243 0.977
 Adjusted R2 0.684
Open in a new tab

The COVID-19-related lockdowns in Lithuania were associated with a statistically significant increase in the relative proportion of alcohol-related traffic collisions and injuries (Table 2), by 3.21% (95% CI: 1.19%, 5.23%) and 2.46% (95% CI: 0.12%, 4.80%), respectively. The association between the COVID-19-related lockdowns and the relative proportion of alcohol-related traffic fatalities was not statistically significant, but was in the hypothesized direction.

Table 2.

Effects of COVD-19-related lockdowns on the relative proportion of alcohol-related traffic collisions, injuries, and fatalities, 2004–2023

Effect estimate SE 95% CI P-value
Lower Upper
Traffic collisions
Intercept 2.493 1.414 −0.278 5.264 0.079
Time 0.056 0.012 0.032 0.080 <0.001
COVID-19 lockdown 3.213 1.031 1.192 5.234 0.002
Alcohol policies/financial crisis, 2008–09 2.759 0.532 1.716 3.802 <0.001
Alcohol policies, 2017–18 −1.077 0.968 −2.974 0.820 0.267
 Adjusted R2 0.549
Traffic injuries
Intercept 2.186 1.723 −1.191 5.563 0.206
Time 0.067 0.015 0.038 0.096 <0.001
COVID-19 lockdown 2.460 1.193 0.122 4.798 0.040
Alcohol policies/financial crisis, 2008–09 2.705 0.613 1.504 3.906 <0.001
Alcohol policies, 2017–18 −0.671 1.146 −2.917 1.575 0.559
 Adjusted R2 0.541
Traffic fatalities
Intercept 7.166 3.573 0.163 14.169 0.046
Time 0.061 0.033 −0.004 0.126 0.063
COVID-19 lockdown 3.636 4.032 −4.267 11.539 0.368
Alcohol policies/financial crisis, 2008–09 0.704 2.207 −3.622 5.030 0.750
Alcohol policies, 2017–18 −0.192 3.376 −6.809 6.425 0.955
 Adjusted R2 0.125
Open in a new tab

The 2008–09 period of alcohol control policy implementation and the financial crisis was found to be associated with a statistically significant increase in the relative proportion of alcohol-related traffic collisions and injuries: 2.76% (95% CI: 1.72%, 3.80%) and 2.71% (95% CI: 1.50%, 3.91%), respectively.

Discussion

The current study found that in Lithuania the COVID-19-related lockdowns were associated with a decrease in the rate of traffic collisions and injuries, and an increase in the relative proportion of alcohol-related traffic collisions and injuries. These findings support the hypotheses, and could be explained in part by the suspected polarization of drinking that took place during this time–wherein light drinkers consumed less alcohol during the lockdowns, but heavy drinkers consumed more–given that traffic injuries have been shown to be predominantly related to heavy alcohol use (Cherpitel et al., 2015, Taylor et al., 2010). The above relationships were in the same direction for traffic fatalities; however, they were not statistically significant, likely because of the high number of zeros in the data for this particular outcome. The decrease in the overall rate of traffic collisions and injuries associated with the lockdowns is consistent with the literature (e.g., see (Shaik ME and Ahmed S, 2022). Despite the relative scarcity of studies on alcohol-related traffic safety during the pandemic, the finding that the lockdowns were associated with an increase in the proportion of alcohol-related traffic collisions and injuries is also consistent with the literature. For instance, Thomas and colleagues (Thomas et al., 2020) found a significantly higher overall prevalence of alcohol use in seriously and fatally injured road users in the United States during the pandemic, compared to before. Another potential explanation that should be acknowledged is that there could have been changes in behavior with regard to reporting minor accidents during COVID. However, the proportion of traffic injuries attributable to alcohol increases as the severity of the incident outcome increases (with death being the most severe outcome) (Taylor et al., 2010, Cherpitel and Ye, 2008). If alcohol was involved, the collisions would have been more likely to be more serious, and thus, reported.

Interestingly, the 2008–09 period of alcohol policy implementation and financial crisis was found to be statistically significantly associated with an increase in the proportion of alcohol-related traffic collisions and injuries, which seems to be counterintuitive. However, the global recession was a time when a polarization of drinking was observed (de Goeij et al., 2015). For instance, Bor and colleagues (Bor et al., 2013) found that in the United States, compared to 2006–07, the prevalence of any alcohol use decreased in 2008–09, but the prevalence of frequent binge drinking increased.

The findings of the current study should be interpreted with the following limitations in mind. First, causality cannot be established with the study design and use of ecological data; thus, we have avoided the use of causal language throughout. Second, the data obtained do not specify the age and/or sex of the individuals involved in the traffic-related incidents. Such information would be beneficial in determining whether the effects observed were consistent across the sexes and age groups. The literature on the global financial recession points towards the polarization of drinking behavior during times of heightened psychological distress being a male phenomenon (de Goeij et al., 2015). Third, as specified above, it is likely that there was less traffic overall during the lockdown periods. However, it was not possible to account for this is the rates of traffic collisions, injuries and fatalities, and thus, the denominator used represents that population at risk, irrespective of the likely reduction in risk due to a lower likelihood of being in a vehicle and reduced traffic.

In summary, the COVID-19-related national lockdowns that occurred in Lithuania, were associated with a decrease in the overall rate of traffic collisions and injuries, and an increase in the relative proportion of alcohol-related traffic collisions. The findings suggest that during future pandemics, or other global crises, targeted enforcement strategies focusing on impaired driving May Be Warranted To Prevent Alcohol-Related Traffic Incidents.

Funding Statement:

Research reported in this publication was supported by the National Institute on Alcohol Abuse and Alcoholism of the National Institutes of Health under Award Number R01AA028224. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funder had no involvement in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

Footnotes

Conflicts of Interest: None to declare.

References

  1. Acuff SF, Strickland JC, Tucker JA & Murphy JG (2022). Changes In Alcohol Use During Covid-19 And Associations With Contextual And Individual Difference Variables: A Systematic Review And Meta-Analysis. Psychology Of Addictive Behaviors, 36, 1–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bor J, Basu S, Coutts A, Mckee M & Stuckler D (2013). Alcohol Use During The Great Recession Of 2008–2009. Alcohol and Alcoholism, 48, 343–8. [DOI] [PubMed] [Google Scholar]
  3. Calderon-Anyosa RJ & Kaufman JS (2021). Impact Of Covid-19 Lockdown Policy On Homicide, Suicide, And Motor Vehicle Deaths In Peru. Preventive Medicine, 143, 106331. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Cherpitel CJ & Ye Y (2008). Alcohol-Attributable Fraction For Injury In The U.S. General Population: Data From The 2005 National Alcohol Survey. Journal of Studies on Alcohol and Drugs, 69, 535–8. [DOI] [PubMed] [Google Scholar]
  5. Cherpitel CJ, Ye Y, Bond J, Borges G & Monteiro M (2015). Relative Risk Of Injury From Acute Alcohol Consumption: Modeling The Dose-Response Relationship In Emergency Department Data From 18 Countries. Addiction, 110, 279–88. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. De Goeij MC, Suhrcke M, Toffolutti V, Van De Mheen D, Schoenmakers TM & Kunst AE (2015). How Economic Crises Affect Alcohol Consumption And Alcohol-Related Health Problems: A Realist Systematic Review. Social Science & Medicine, 131, 131–46. [DOI] [PubMed] [Google Scholar]
  7. González-Monroy C, Gómez-Gómez I, Olarte-Sánchez CM & Motrico E (2021). Eating Behaviour Changes During The Covid-19 Pandemic: A Systematic Review Of Longitudinal Studies. International Journal of Environmental Research and Public Health, 18, 11130. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Harrell FE Jr. (2015). Regression Modeling Strategies: With Applications To Linear Models, Logistic And Ordinal Regression, And Survival Analysis, New York, Springer. [Google Scholar]
  9. Jiang H, Rehm J, Tran A & Lange S (2024). Interrupted Time Series Design and Analyses in Health Policy Assessment. Medrxiv, 2024.08.01.24311280. [Google Scholar]
  10. Kilian C, O’donnell A, Potapova N, Lopez-Pelayo H, Schulte B, Miquel L, et al. (2022). Changes In Alcohol Use During The Covid-19 Pandemic In Europe: A Meta-Analysis Of Observational Studies. Drug and Alcohol Review, 41, 918–931. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Kriaucioniene V, Bagdonaviciene L, Rodríguez-Pérez C & Petkeviciene J (2020). Associations Between Changes In Health Behaviours And Body Weight During The Covid-19 Quarantine In Lithuania: The Lithuanian Covidiet Study. Nutrients, 12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Lietuvos Kelių Policijos Tarnyba [Lithuanian Road Police Service] (2024). Eismo Įvykių Lietuvoje Statistika [Statistics Of Traffic Accidents In Lithuania].
  13. Miščikienė L, Midttun NG, Galkus L, Belian G, Petkevičienė J, Vaitkevičiūtė J, et al. (2020). Review Of The Lithuanian Alcohol Control Legislation In 1990–2020. International Journal of Environmental Research and Public Health, 17, 3454. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. R Core Team. (2023). R: A Language And Environment For Statistical Computing. Version 4.2.3. Vienna, Austria: R Foundation For Statistical Computing. [Google Scholar]
  15. Rehm J, Badaras R, Ferreira-Borges C, Galkus L, Gostautaite Midttun N, Gobiņa I, et al. (2023a). Impact Of The Who “Best Buys” For Alcohol Policy On Consumption And Health In The Baltic Countries And Poland 2000–2020. The Lancet Regional Health - Europe, 33, 100704. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Rehm J, Kilian C, Ferreira-Borges C, Jernigan D, Monteiro M, Parry CDH, et al. (2020a). Alcohol Use In Times Of The Covid 19: Implications For Monitoring And Policy. Drug and Alcohol Review, 39, 301–304. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Rehm J, Lange S, Gobiņa I, Janik-Koncewicz K, Miščikienė L, Reile R, et al. (2023b). Classifying Alcohol Control Policies Between 2000 And 2020 In Poland And The Baltic Countries To Model Potential Impact. Addiction, 118, 449–458. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Rehm J, Manthey J, Lange S, Badaras R, Zurlyte I, Passmore J, et al. (2020b). Alcohol Control Policy and Changes In Alcohol-Related Traffic Harm. Addiction, 115, 655–665. [DOI] [PubMed] [Google Scholar]
  19. Rehm J, Rovira P, Jiang H, Lange S, Shield KD, Tran A, et al. (2024). Trends of Alcohol-Attributable Deaths In Lithuania 2001–2021: Epidemiology and Policy Conclusions. BMC Public Health, 24, 774. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Rehm J & Štelemėkas M (2023). Evaluating The Impact of Covid-19 Public Health Measures on Alcohol-Related Harms (R01AA028224). National Institute on Alcohol Abuse and Alcoholism. [Google Scholar]
  21. Roberts A, Rogers J, Mason R, Siriwardena AN, Hogue T, Whitley GA, et al. (2021). Alcohol And Other Substance Use During The Covid-19 Pandemic: A Systematic Review. Drug And Alcohol Dependence, 229, 109150. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Schmidt RA, Genois R, Jin J, Vigo D, Rehm J & Rush B (2021). The Early Impact of COVID-19 on the Incidence, Prevalence, and Severity of Alcohol Use and Other Drugs: A Systematic Review. Drug And Alcohol Dependence, 228, 109065. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Seimas Of The Republic Of Lithuania. (2000). Law Of The Republic Of Lithuania On Safe Traffic For Cars On Roads [Lietuvos Respublikos Saugaus Eismo Automobilių Keliais Įstatymas]. No. Viii-2043. Vilnius, Lithuania: Register Of Legal Deeds. [Google Scholar]
  24. Shaik ME & Ahmed S (2022). An Overview Of The Impact Of Covid-19 On Road Traffic Safety And Travel Behavior. Transportation Engineering, 9, 100119. [Google Scholar]
  25. Sohi I, Chrystoja BR, Rehm J, Wells S, Monteiro M, Ali S et al. (2022). Changes In Alcohol Use During The Covid-19 Pandemic And Previous Pandemics: A Systematic Review. Alcohol: Clinical And Experimental Research, 46, 498–513. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Taylor B, Irving HM, Kanteres F, Room R, Borges G, Cherpitel C, et al. (2010). The More You Drink, The Harder You Fall: A Systematic Review And Meta-Analysis Of How Acute Alcohol Consumption And Injury Or Collision Risk Increase Together. Drug and Alcohol Dependence, 110, 108–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Thomas F, Berning A, Darrah J, Graham L, Blomberg R, Griggs C, et al. (2020). Drug And Alcohol Prevalence In Seriously And Fatally Injured Road Users Before And During The Covid-19 Public Health Emergency (Report No. Dot Hs 813 018). Washington, DC: National Highway Traffic Safety Administration. [Google Scholar]
  28. Tran A, Jiang H, Lange S, Štelemėkas M, Stumbrys D, Tamutienė I, et al. (2024). Changes in Self-Reported Alcohol Consumption at High and Low Consumption in the Wake of the COVID-19 Pandemic: A Test of the Polarization Hypothesis. Medrxiv, 2024.07.31.24311291. [Google Scholar]
  29. Wood SN (2003). Thin Plate Regression Splines. Journal Of The Royal Statistical Society Series B: Statistical Methodology, 65, 95–114. [Google Scholar]
  30. World Health Organization. (In Press). Report on the Progress of Attaining SDG Target 3.5. Geneva, Switzerland: World Health Organization. [Google Scholar]

RESOURCES