Trends of alcohol-attributable deaths in Lithuania 2001–2021: epidemiology and policy conclusions

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Abstract

Background

Lithuania, a Baltic country in the European Union, can be characterized by high alcohol consumption and attributable burden. The aim of this contribution is to estimate the mortality burden due to alcohol use for the past two decades based on different relative risk functions, identify trends, and analyse the associations of alcohol-attributable burden with alcohol control policies and life expectancy.

Methods

The standard methodology used by the World Health Organization for estimating alcohol-attributable mortality was employed to generate mortality rates for alcohol-attributable mortality, standardized for Lithuania’s 2021 population distribution. Joinpoint analysis, T-tests, correlations, and regression analyses including meta-regressions were used to describe trends and associations.

Results

Age-standardized alcohol-attributable mortality was high in Lithuania during the two decades between 2001 and 2021, irrespective of which relative risks were used for the estimates. Overall, there was a downward trend, mainly in males, which was associated with four years of intensive implementation of alcohol control policies in 2008, 2009, 2017, and 2018. For the remaining years, the rates of alcohol-attributable mortality were stagnant. Among males, the correlations between alcohol-attributable mortality and life expectancy were 0.90 and 0.76 for Russian and global relative risks respectively, and regression analyses indicated a significant association between changes in alcohol-attributable mortality and life expectancy, after controlling for gross domestic product.

Conclusions

Male mortality and life expectancy in Lithuania were closely linked to alcohol-attributable mortality and markedly associated with strong alcohol control policies. Further implementation of such policies is predicted to lead to further improvements in life expectancy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-024-18237-y.

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Most cited references 52

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Global alcohol exposure between 1990 and 2017 and forecasts until 2030: a modelling study

Jakob Manthey, Kevin Shield, Margaret Rylett … (2019)

Alcohol use is a leading risk factor for global disease burden, and data on alcohol exposure are crucial to evaluate progress in achieving global non-communicable disease goals. We present estimates on the main indicators of alcohol exposure for 189 countries from 1990-2017, with forecasts up to 2030.

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The relationship between different dimensions of alcohol use and the burden of disease—an update

Jürgen Rehm, Gerhard E Gmel, Gerrit Gmel … (2017)

Abstract Background and aims Alcohol use is a major contributor to injuries, mortality and the burden of disease. This review updates knowledge on risk relations between dimensions of alcohol use and health outcomes to be used in global and national Comparative Risk Assessments (CRAs). Methods Systematic review of reviews and meta‐analyses on alcohol consumption and health outcomes attributable to alcohol use. For dimensions of exposure: volume of alcohol use, blood alcohol concentration and patterns of drinking, in particular heavy drinking occasions were studied. For liver cirrhosis, quality of alcohol was additionally considered. For all outcomes (mortality and/or morbidity): cause of death and disease/injury categories based on International Classification of Diseases (ICD) codes used in global CRAs; harm to others. Results In total, 255 reviews and meta‐analyses were identified. Alcohol use was found to be linked causally to many disease and injury categories, with more than 40 ICD‐10 three‐digit categories being fully attributable to alcohol. Most partially attributable disease categories showed monotonic relationships with volume of alcohol use: the more alcohol consumed, the higher the risk of disease or death. Exceptions were ischaemic diseases and diabetes, with curvilinear relationships, and with beneficial effects of light to moderate drinking in people without heavy irregular drinking occasions. Biological pathways suggest an impact of heavy drinking occasions on additional diseases; however, the lack of medical epidemiological studies measuring this dimension of alcohol use precluded an in‐depth analysis. For injuries, except suicide, blood alcohol concentration was the most important dimension of alcohol use. Alcohol use caused marked harm to others, which has not yet been researched sufficiently. Conclusions Research since 2010 confirms the importance of alcohol use as a risk factor for disease and injuries; for some health outcomes, more than one dimension of use needs to be considered. Epidemiological studies should include measurement of heavy drinking occasions in line with biological knowledge.

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A random-effects regression model for meta-analysis.

C S Berkey, D. Hoaglin, F Mosteller … (1995)

Many meta-analyses use a random-effects model to account for heterogeneity among study results, beyond the variation associated with fixed effects. A random-effects regression approach for the synthesis of 2 x 2 tables allows the inclusion of covariates that may explain heterogeneity. A simulation study found that the random-effects regression method performs well in the context of a meta-analysis of the efficacy of a vaccine for the prevention of tuberculosis, where certain factors are thought to modify vaccine efficacy. A smoothed estimator of the within-study variances produced less bias in the estimated regression coefficients. The method provided very good power for detecting a non-zero intercept term (representing overall treatment efficacy) but low power for detecting a weak covariate in a meta-analysis of 10 studies. We illustrate the model by exploring the relationship between vaccine efficacy and one factor thought to modify efficacy. The model also applies to the meta-analysis of continuous outcomes when covariates are present.

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Author and article information

Contributors

Jürgen Rehm: jtrehm@gmail.com

Journal

Journal ID (nlm-ta): BMC Public Health

Journal ID (iso-abbrev): BMC Public Health

Title: BMC Public Health

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2458

Publication date (Electronic): 12 March 2024

Publication date PMC-release: 12 March 2024

Publication date Collection: 2024

Volume: 24

Electronic Location Identifier: 774

Affiliations

[1 ]Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, ( https://ror.org/03e71c577) 33 Ursula Franklin Street, M5S 2S1 Toronto, ON Canada

[2 ]Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, ( https://ror.org/03e71c577) 250 College St, M5T 1R8 Toronto, ON Canada

[3 ]Dalla Lana School of Public Health, University of Toronto, ( https://ror.org/03dbr7087) 155 College Street, M5T 1P8 Toronto, ON Canada

[4 ]Institute of Medical Science, University of Toronto, ( https://ror.org/03dbr7087) Medical Sciences Building, 1 King’s College Circle, Room 2374, M5S 1A8 Toronto, ON Canada

[5 ]Department of Psychiatry, University of Toronto, ( https://ror.org/03dbr7087) 250 College Street, 8th floor, M5T 1R8 Toronto, ON Canada

[6 ]Centre for Interdisciplinary Addiction Research, Department of Psychiatry, University Medical Center Hamburg-Eppendorf, ( https://ror.org/01zgy1s35) Martinistr. 52, 20246 Hamburg, Germany

[7 ]Program on Substance Abuse & WHO CC, Public Health Agency of Catalonia, ( https://ror.org/0301ppm60) 81-95 Roc Boronat St, 08005 Barcelona, Spain

[8 ]Health Research Institute, Faculty of Public Health, Lithuanian University of Health Sciences, ( https://ror.org/0069bkg23) Tilzes str. 18, 47181 Kaunas, Lithuania

[9 ]Department of Preventive Medicine, Faculty of Public Health, Lithuanian University of Health Sciences, ( https://ror.org/0069bkg23) Tilzes str. 18, 47181 Kaunas, Lithuania

Article

Publisher ID: 18237

DOI: 10.1186/s12889-024-18237-y

PMC ID: 10935848

PubMed ID: 38475821

SO-VID: b32a4994-3797-4055-81bd-eef217b5c3c2

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 30 August 2023

Date accepted : 29 February 2024

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000027, National Institute on Alcohol Abuse and Alcoholism;

Award ID: R01AA028224

Custom metadata

ScienceOpen disciplines: Public health

Keywords: alcohol,attributable deaths,fully attributable,partially attributable,alcohol-attributable deaths,gender,trends,alcohol control policies,lithuania

Data availability:

ScienceOpen disciplines: Public health

Keywords: alcohol, attributable deaths, fully attributable, partially attributable, alcohol-attributable deaths, gender, trends, alcohol control policies, lithuania

Trends of alcohol-attributable deaths in Lithuania 2001–2021: epidemiology and policy conclusions

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Abstract

Background

Methods

Results

Conclusions

Supplementary Information

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Global Health Next Generation Network

Most cited references 52

Global alcohol exposure between 1990 and 2017 and forecasts until 2030: a modelling study

The relationship between different dimensions of alcohol use and the burden of disease—an update

A random-effects regression model for meta-analysis.

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