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. Author manuscript; available in PMC: 2024 Feb 1.
Published in final edited form as: Drug Alcohol Rev. 2022 Dec 13;42(2):487–494. doi: 10.1111/dar.13584

Are restrictions in sales hours of alcohol associated with fewer emergency room visits in Lithuania? An interrupted time-series analysis

Huan Jiang 1,2, Alexander Tran 1, Janina Petkevičienė 3,4, Mindaugas Štelemėkas 3,4, Shannon Lange 1,5,6, Jürgen Rehm 1,2,5,6,7,8,9,10,11
PMCID: PMC9898194  NIHMSID: NIHMS1858968  PMID: 36514305

Abstract

Introduction:

On January 1, 2018, an amendment to the alcohol control law was introduced in Lithuania which, among other changes, reduced trading hours for alcoholic beverages by four hours for weekdays and Saturdays, and by nine hours for Sundays. The objective of the current study was to quantify the potential association of this law with the numbers and types of emergency room (ER) visits in Lithuania, in general and specifically for Sundays, for all ER visits, for injury-related ER visits and specifically for alcohol poisoning as a 100% alcohol-attributable cause.

Methods:

Sex-stratified time-series analysis-based models for the period 2016-2019 were used to test for associations and for potential alternative explanations (e.g., the increase in minimum legal drinking age, which occurred at the same time).

Results:

Overall, while the reduction in sales hours for both sexes was associated with slight increases in all types and in injury-related ER visits on a weekly basis, the association with ER visits for alcohol poisoning was in the opposite direction for men in all models. Specifically, among men, it was associated with an approximate decrease of 20% of alcohol poisoning-related ER visits on Sundays, and an approximate decrease of 12% of alcohol poisoning-related ER visits for all seven weekdays.

Discussion and Conclusions:

As predicted, restrictions on availability were associated with marked reductions in ER visits for alcohol poisoning in men. However, contrary to expectations, there were no overall reductions in overall ER visits, nor reductions in injury-related ER visits.

Keywords: alcohol, availability, hours of sale, emergency room visits, Lithuania, alcohol control policy

Introduction:

Decreasing availability of alcoholic beverages constitutes one of the three “best buys” in alcohol control policy, as defined by the World Health Organization (1). “Best buys” have been defined as interventions which are not only highly cost-effective in reducing alcohol-attributable harm but also feasible and appropriate to implement (2). While the term “best buy” was coined for the framework to monitor and control the most prevalent non-communicable diseases, earlier work demonstrated that the three best buys for alcohol, i.e., increasing prices via taxation, restricting availability, and banning marketing were effective and cost-effective in reducing alcohol-attributable burden of disease in general, which, in addition to non-communicable diseases, includes infectious disease categories such as lower respiratory infections, and unintentional and intentional injuries (for an overview of disease categories related to alcohol use, see (3); for the original demonstration of cost-effectiveness, see (4)). The effectiveness and cost-effectiveness of the best buys have recently been corroborated by a study which included 16 countries with varying income levels as per The World Bank (5).

The available evidence from existing systematic reviews suggests that changes in availability via the extension of trading hours for alcohol are associated with increases in consumption and attributable harm, whereas restrictions on trading hours have the opposite effect (6-9). Much of the underlying literature has been about on-premise drinking and the link to injury in general, and interpersonal violence in particular (for details see (8); see also (10) for a modelling study), but the review of Nepal and colleagues (6) also identified two studies in European Union (EU) countries on restrictions on off-premise sales hours which were associated with decreases in hospitalizations and assaults (11, 12).

While studies suggest that decreasing the availability via reducing off-premise trading hours will decrease per capita consumption (13), questions have been raised regarding the relevance of traditional forms of availability, such as hours of sale for alcoholic beverages, in the second or third decade of the 21st century, especially in high-income countries (such as countries in the EU). In these countries, alternatives to off-premise sales are readily available, including on-premise drinking and internet orders, as well as stockpiling, which may be an option for a large proportion of the population given the high affordability of alcoholic beverages in EU countries.(14) However, depending on the specific jurisdiction and its particular laws, the alternatives may be more expensive than usual off-premise sales.

Thus, in the current study, we used a recent change to a law in Lithuania—which decreased off-premise sales hours from Monday to Saturday by four hours (from 8:00-22:00 to 10:00-20:00; a 29% reduction), and on Sundays by nine hours (from 8:00-22:00 to 10:00-15:00; a 64% reduction) starting on January 1, 2018 (15)—to test whether availability restrictions via off-premise sales hours still have a significant impact on alcohol-attributable harm in a high-income country in today’s world. Alcohol-attributable harm was operationalized via visits to the emergency room (ER). This outcome was selected since availability, as indicated above, seems to be especially linked to acute outcomes. Lithuania is a good place for testing this hypothesis, as it still has the traditional drinking pattern of north-eastern European countries, characterized by irregular heavy drinking occasions (16-18), despite some reductions in recent years (19).

Based on availability theory (13, 20), we postulated that the reduction in availability would lead to less alcohol consumption especially in the evening, evidenced by an immediate reduction in alcohol-attributable acute harm, such as various forms of injury presenting to the ER. Concretely, we hypothesized that, first, reduced availability of alcoholic beverages would lead to fewer ER visits in general, and in fewer injury-related and alcohol-poisoning ER visits in particular. Injury is the typical outcome for studies on availability (see reviews above) and alcohol poisoning was added as a typical marker for 100% alcohol-attributable burden in the region; (21, 22)). Second, we postulated that the effect would be most pronounced on Sundays and Mondays, where the availability restrictions were the most pronounced (Monday was added as alcohol-attributable ER visits are most notable during nighttime—e.g. (23)—to include the full night following Sunday).

On the same day that sales hours were decreased (i.e., January 1, 2018), there was also a change to the minimum legal drinking age (MLDA) from 18 to 20 years of age with stricter enforcement.(15) To exclude the possibility that the changes found in ER visits were driven by people affected by the change in MLDA, we conducted similar analyses for those individuals specifically affected by this change, i.e., 18- and 19-year-olds. The only other alcohol control policy that was implemented on that date concerned a ban on all alcohol marketing. This policy change is expected to have more long-term effects, especially on youth (e.g., (24, 25)), but not necessarily a noticeable immediate effect on reducing consumption and ER visits on the population level ((13); see also the mixed results of econometric studies concerning the general population effects (24, 26, 27)). Thus, no additional analyses were conducted to try to separate out the effects of this policy.

Methods

Data

Daily ER data were obtained from the Lithuanian Institute of Hygiene (28) for the period from January 2, 2016 to December 30, 2019. It should be recognized that the International Classification of Diseases, version 10 (ICD-10) codes for causes related to ER visits are somewhat imprecise, due to the ER’s high-stress and high-intensity work environment. In addition, the causes of ER visits were not provided in all cases. For these reasons, we included all ER visits in the first analysis. In the second analysis, the dependent variable was all injury codes, operationalized as all ICD-10 codes beginning with S or T, with the exception of alcohol poisoning (i.e., ICD-10 Chapter XIX: Injury, poisoning and certain other consequences of external causes S00-T98; (29)), and, in the third analysis ICD-10 codes T51 (T51.0, T51.1, T51.2, T51.3, T51.8, T51.9), X45, F10.0 as an operationalization for alcohol poisoning. In 2018, injuries as operationalized above, constituted 15% of all the ER visits (18% in men; 12% in women).

Patient and Public Involvement

Patients and the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research.

Dependent variable

As indicated above, the main dependent variables were the number of total and alcohol poisoning-related ER visits for those ages 15 years and above (count data). We analyzed ER visits on Sundays and Mondays collapsed (as anything after 23:59 on Sunday would be recorded as Monday), and additionally collapsed all ER visits into a weekly time-series dataset. As a control series for Sundays and Mondays, we also analyzed data from visits on Wednesdays and Thursdays. We conducted one additional analysis including only ER visits for 18- and 19-year-olds on Sundays and Mondays to test the hypothesis that the observed effect of the alcohol availability policy was mainly due to the change in MLDA.

Intervention

The underlying policy intervention of restricting off-premise hours of sale was implemented on January 1, 2018 together with raising the MLDA and a ban on marketing (15, 30). This set of interventions was dummy-coded as 0 before the implementation date and as a 1 after the implementation.

Statistical analyses

To address our hypothesis that alcohol control policies can reduce ER visits, we performed interrupted time-series analyses by employing a generalized additive mixed model (GAMM (31)) for each of the outcomes. In the GAMM, a Poisson distribution was used to model count data with offsets to account for the changes in population. Seasonality was adjusted for by adding smoothing splines representing daily, weekly, or monthly patterns. Residuals were examined with plots of the autocorrelation function and the partial autocorrelation function to determine the orders of autoregressive and moving average series and were controlled for using the Box-Jenkins method. Separate GAMMs were applied on weekly data and on the data only including Sunday and Monday. To investigate how the control series of Wednesday and Thursday visits differ from the series of Sunday and Monday visits, GAMMs were applied to adjust for the control series by including the indicator variable (for the control series) and its interaction with the policy.

GAMM with Poisson family generates the maximum-likelihood estimates of the regression coefficients. The direct interpretation of model coefficient is difficult because the formula for the predicted value involves the exponential function, but they can be interpreted approximately as percentage change in counts after transforming using the formula: exp(coefficient) −1 (32).

In a subgroup sensitivity analysis, GAMMs were applied to the number of ER visits for 18- and 19-year-olds on Sundays and Mondays to investigate its potential contribution to the overall results.

All analyses were performed using R version 4.0.5 (33).

Results

Between January 2, 2016 and December 30, 2019, there were 910,720 ER visits on Sundays and Mondays. Among them, 0.94% (8,606) were due to alcohol poisoning (see Appendix). Figure 1 plots of the numbers of all causes of ER visits on Sundays and Mondays across time by sex, which shows a pattern that occurs at a regular interval and strong evidence of seasonal variation.

Figure 1:

Figure 1:

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Number of all-cause ER visits on Sunday and Monday across time: the red line represents the date of the policy implementation

For all ER visits, GAMMs with Poisson distribution showed a slight, but statistically significant, increasing trend over time for both men (Table 1) and women (Table 2) across all three sets of models. In contrast, for both alcohol poisoning-related and injury-related ER visits, a decreasing trend over time was found for both sexes. However, the trend did not show significant changes after the policy implementation, e.g., the coefficient of interactions between time and policy was 0.0002 (P=0.0679) for all ER visits.

Table 1:

Model statistics for the effects of the reduction in sales hours for alcoholic beverages on ER visits for men

All ER visits Alcohol poisoning-related ER visits Injury related ER visits
Estimate Std. Error Pr(>∣t∣) Estimate Std. Error Pr(>∣t∣) Estimate Std. Error Pr(>∣t∣)
Visits on Sunday and Monday
(Intercept) −6.3142 0.0047 <0.001 −10.2872 0.0348 <0.001 −7.8875 0.0104 <0.001
Time 0.0003 0.0001 0.0001 −0.0008 0.0006 0.1569 −0.0005 0.0002 0.0089
Policy −0.0031 0.0128 0.8109 −0.2221 0.1068 0.0389 0.0198 0.0296 0.5052
Time*Policy 0.0002 0.0001 0.0679 0.0007 0.0008 0.4221 <0.001 0.0002 0.8836
R-square   0.6136   0.2066     0.6996  
Visits per week
(Intercept) −5.1304 0.0028 <0.001 −9.0764 0.0176 <0.001 −6.7025 0.0061 <0.001
Time 0.0004 4.70E-05 <0.001 −0.0014 0.0003 <0.001 −0.0007 0.0001 <0.001
Policy 0.0353 0.0077 <0.001 −0.1202 0.0533 0.0252 0.0518 0.0175 0.0035
Time*Policy −0.0001 0.0001 0.0287 0.0007 0.0004 0.1012 −0.0001 0.0001 0.4095
R-square   0.526   0.3727   0.7834  
Visits on Sunday and Monday (Sun&Mon) including Wednesday and Thursday as control series
(Intercept) −6.4441 0.0048 <0.001 −10.4295 0.0287 <0.001 −8.0701 0.0094 <0.001
Time 0.0004 0.0001 <0.001 −0.0018 0.0004 <0.001 −0.0004 0.0001 0.0018
Sun&Mon 0.1246 0.004 <0.001 0.1925 0.0244 <0.001 0.1857 0.0079 <0.001
Policy 0.0064 0.0121 0.5993 −0.1833 0.0809 0.024 0.0520 0.0248 0.0366
Sun&Mon*Policy −0.0170 0.0057 0.0028 −0.0296 0.0366 0.4182 −0.0184 0.0114 0.1059
Time*Policy 0.0002 0.0001 0.0543 0.0013 0.0006 0.0378 −0.0001 0.0002 0.4566
R-square   0.7427     0.2971     0.7392  
Open in a new tab
“*”

refers to the interaction between the two variables.

Table 2:

Model statistics for the effects of the reduction in sales hours for alcoholic beverages on ER visits for women

All ER visits Alcohol poisoning-related ER visits Injury related ER visits
Estimate Std. Error Pr(>∣t∣) Estimate Std. Error Pr(>∣t∣) Estimate Std. Error Pr(>∣t∣)
Visits on Sunday and Monday
(Intercept) −6.4795 0.0064 <0.001 −11.6886 0.0658 <0.001 −8.5006 0.0139 <0.001
Time 0.0011 0.0001 <0.001 −0.0032 0.0011 0.0061 0.0002 0.0002 0.4432
Policy 0.0460 0.0170 0.0073 0.0476 0.2011 0.8133 0.0924 0.0384 0.0169
Time*Policy −0.0003 0.0001 0.0566 0.0009 0.0016 0.5853 −0.0004 0.0003 0.215
R-square 0.6151 0.1734 0.6222
Visits per week
(Intercept) −5.2902 0.0031 <0.001 −10.5618 0.0368 <0.001 −7.3475 0.0082 <0.001
Time 0.0012 0.0001 <0.001 −0.0018 0.0006 0.0052 0.0002 0.0001 0.1193
Policy 0.0854 0.0081 <0.001 0.1516 0.1115 0.1756 0.1272 0.0226 <0.001
Time*Policy −0.0005 0.0001 <0.001 −0.0008 0.0009 0.3506 −0.0007 0.0002 0.0003
R-square   0.521   0.2796   0.6504  
Visits on Sunday and Monday (Sun&Mon) including Wednesday and Thursday as control series
(Intercept) −6.5798 0.0044 <0.001 −11.8921 0.0553 <0.001 −8.6994 0.0111 <0.001
Time 0.0011 0.0001 <0.001 −0.0031 0.0008 0.0003 0.0003 0.0002 0.0942
Sun&Mon 0.101 0.0037 <0.001 0.202 0.0474 <0.001 0.1969 0.0092 <0.001
Policy 0.0531 0.0108 <0.001 0.0763 0.1544 0.6212 0.1037 0.028 0.0002
Sun&Mon*Policy −0.0212 0.0051 <0.001 −0.0029 0.0704 0.9675 −0.0015 0.013 0.9057
Time*Policy −0.0002 0.0001 0.0417 0.0007 0.0012 0.57 −0.0005 0.0002 0.0201
R-square 0.7143 0.1887 0.6434
Open in a new tab
“*”

refers to the interaction between the two variables.

Effects of the policy

For men, while the implementation of the policy did not show a consistent impact on all ER visits across models, it was associated with a reduction of alcohol poisoning-related ER visits across all models (Table 1): for the model including only Sundays and Mondays, the policy led to an approximate decrease of 20% (exp(−0.2221)−1 = −0.1991, P=0.0389) of alcohol poisoning-related ER visits; for the model including weekly visits, the policy led to a decrease of 12% (P=0.0252) of alcohol poisoning-related ER visits.

When the control series of Wednesday and Thursday was included into the model, ER visits on Sunday and Monday were significantly different from the control series (P<0.001). Specifically, the policy led to a smaller increase of all ER visits on Sunday and Monday than Wednesday and Thursday (P=0.0028). After the implementation of the policy, alcohol poisoning-related ER visits were reduced by around 18% (P=0.024) even though there were about 19% (P<0.001) more visits on Sundays and Mondays than there were on Wednesdays and Thursdays.

On the contrary, the policy was associated with significant increases in injury-related ER weekly visits (P=0.0035) and on the visits on Sunday and Monday after adjusting using a control series of Wednesday and Thursday (P=0.0366).

For women, the policy was related to significant increases of overall ER visits in all three models. However, there was a slope change: after the policy was implemented, the overall increase slowed down. When the control series for Sunday and Monday was included in the model, the policy acted differently on the Sunday-and-Monday series: a decrease of 2.1% (exp(−0.0212)−1 = −0.021; P=<0.001) was estimated compared to Wednesday and Thursday. The policy had no impact on alcohol poisoning-related ER visits, while it appeared to have significantly positive impacts on injury-related ER visits for all three sets of models: increases of 9.7%, 13.6% and 10.9%, respectively.

As a sensitivity analysis, ER visits on Sundays and Mondays by those aged 18 and 19 years old were examined using GAMMs (see the Appendix) to test the possible effect of increasing MLDA. The policy showed no significant impacts; that is, the resulting models show the increase of MLDA were not associated with changes in ER visits on Sundays and Mondays.

Discussion

Overall, we found a time trend of increasing ER visits over the time period between 2016 and 2019. The implementation of the policy was associated with small increases in all-cause and injury-related ER visits for both sexes, but proportionally larger decreases in ER visits for alcohol poisonings for men. Thus, we did not find any indication of the postulated reductions in ER visits overall or for injury-related ER visits, but evidence that reduced availability was associated with a reduction of alcohol-poisoning ER visits for men.

Before we discuss these results further, we would like to point out some limitations to the study: while direct methods to assess alcohol policy via methodology based on time-series clearly are the gold standard for establishing statistical control (34), other explanations cannot be excluded. We assessed two policies implemented on the same day. The introduction of a MLDA was not found to have contributed to the results. The marketing and advertising ban was not postulated to have immediate effects for theoretical reasons, as its effects would cumulate over time, especially for adolescents and young adults (see above). Any effect would have resulted in a reduction in overall ER visits, which was clearly not the case in Lithuania in 2018. In addition, there appears to have been additional factors that influence ER visits which may have played a role, as the values for explained variance indicate. Also, further studies are necessary to identify the trend in increasing ER visits in general over the time period.

Despite these limitations, our study clearly does not seem to support the hypothesis that overall ER or injury-related ER visits were reduced by decreasing availability via reducing trading hours. It may well be that ERs in today's environment are functioning in a way that the overall ER volume will be reached, and if certain disease categories are reduced, then others are increased. Such policies were confirmed by informal interviews with the appropriate hospital personnel. Other than on mortality, where we clearly found an impact of alcohol policy on all-cause mortality (e.g., (34-36)), the effect of alcohol control policies on health services may not result in overall reductions in ER visits or hospitalizations, as healthcare services may adapt and maintain or increase their overall volumes (e.g., accommodate those with relatively minor injuries or disease conditions who would have previously been turned away).

However, we saw a reduction in alcohol poisoning-related ER visits for men (and no changes in alcohol poisoning-related ER visits in women). Sex differences for this effect are plausible: first, more men use alcohol typically at much higher levels in all countries ((16); for a typical high-income country, see (37)), and, second, for very heavy drinking leading to alcohol poisoning, sex differences are even more pronounced. Thus, in Lithuania over the time period observed, there were 3.7 times more alcohol-poisoning deaths, and 3.0 times more hospitalizations for alcohol poisoning in men compared to women (own calculations; details not shown), despite the fact that women achieve higher concentrations of alcohol in their blood and become more impaired than men after drinking equivalent amounts of alcohol (38).

In conclusion, restrictions in trading hours seem to have effects on alcohol-attributable ER visits, but not on the overall number or on injury-related ER visits. More research is necessary to investigate potential substitution effects to understand how ERs deal with a decrease in the number of individuals presenting to hospital with certain disease categories; such knowledge would have significant implications for the interpretation of studies such as the current one. Further, our findings have implications for the costs of alcohol use (39, 40). While there were fewer ER visits due to alcohol poisoning, this may not be associated with reductions in economic costs, as other types of ER visits increased. Finally, we need more research into other alcohol-attributable ER visits to give more focused recommendations. In addition, limitations on sales for on-premise establishments should be considered (10, 41). However, the effectiveness of availability restrictions should continue to be monitored in light of the changing market for alcohol sales not only in Lithuania, but in high-income countries generally. The COVID-19 pandemic has brought a widening of sales options via the internet in many countries, including some possibilities of delivery violating the restrictions on ordering (42). Depending on the prices and magnitude of such deliveries when compared to off-premise prices, such changes may decrease the effectiveness of sales hour restrictions in the future.

Supplementary Material

Supplementary material

Acknowledgments:

We would like to acknowledge Astrid Otto for referencing and copy-editing the manuscript

Research reported in this publication was supported by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) of the National Institutes of Health under grant 1R01AA028224 and was conducted as part of the project "Evaluation of the impact of alcohol control policies on morbidity and mortality in Lithuania and other Baltic states".

Grant:

The NIAAA grant “Evaluation of the impact of alcohol control policies on morbidity and mortality in Lithuania and other Baltic states” (Award Number 1R01 AA028224) supported the research for this paper. However, it played no role in the design, data collection, analyses, interpretation, writing, or decision to submit this article.

List of abbreviations in order of appearance

ER

emergency room

MLDA

minimum legal drinking age

ICD

International Classification of Diseases

GAMM

general additive mixed model

ARIMA

autoregressive integrated moving average

Footnotes

Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Ethics approval and consent to participate: Third party anonymized database; approval from the CAMH, Research Ethics Board (REB# 050/2020).

Data availability:

The original data are administrative data of the Lithuanian government agencies and need to be obtained directly from the original source (exact sources are indicated in the article).

Data availability statement

The data used in the current study can be obtained by request through the Lithuanian governmental institutions (Lithuanian Institute of Hygiene, Statistics Lithuania). The R Code used to analyze and compute variables for the current study is available from the corresponding author upon request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material
NIHMS1858968-supplement-Supplementary_material.docx (1.6MB, docx)

Data Availability Statement

The original data are administrative data of the Lithuanian government agencies and need to be obtained directly from the original source (exact sources are indicated in the article).

The data used in the current study can be obtained by request through the Lithuanian governmental institutions (Lithuanian Institute of Hygiene, Statistics Lithuania). The R Code used to analyze and compute variables for the current study is available from the corresponding author upon request.

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