Alexander Tran1, Huan Jiang1,2, Kawon Victoria Kim1,2, Robin Room3,4, Mindaugas Štelemėkas5,6, Shannon Lange1,7, Pol Rovira8, Jürgen Rehm1,2,7,8,9,10,11,12,13. 1. Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, 33 Ursula Franklin Street, Toronto, Ontario M5S 2S1, Canada. 2. Epidemiology Division, Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, Ontario M5T 1P8, Canada. 3. Centre for Alcohol Policy Research, Building NR-1, La Trobe University, Bundoora, Victoria 3086, Australia. 4. Department of Public Health Sciences, Centre for Social Research on Alcohol and Drugs, Department of Public Health Sciences, Albanovägen 12, floor 5, Stockholm University, Stockholm 106 91, Sweden. 5. Health Research Institute, Faculty of Public Health, Lithuanian University of Health Sciences, Tilžės str. 18, Kaunas 47181, Lithuania. 6. Department of Preventive Medicine, Faculty of Public Health, Lithuanian University of Health Sciences, Tilžės str. 18, Kaunas 47181, Lithuania. 7. Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Ursula Franklin Street 33, Toronto, OntarioM5S 3M1, Canada. 8. Program on Substance Abuse, Public Health Agency of Catalonia, 81-95 Roc Boronat St., Barcelona 08005, Spain. 9. Institute of Clinical Psychology and Psychotherapy, Technische Universität Dresden, Chemnitzer Str. 46, Dresden 01187, Germany. 10. Department of Psychiatry and Psychotherapy, Center for Interdisciplinary Addiction Research (ZIS), University Medical Center Hamburg-Eppendorf (UKE), Martinistraße 52, Hamburg 20246, Germany. 11. Faculty of Medicine, Institute of Medical Science, University of Toronto, Medical Sciences Building, 1 King's College Circle, Room 2374, Toronto, Ontario M5S 1A8, Canada. 12. Department of Psychiatry, University of Toronto, 250 College Street, 8th floor, Toronto, Ontario M5T 1R8, Canada. 13. Department of International Health Projects, Institute for Leadership and Health Management, I.M. Sechenov First Moscow State Medical University (Sechenov University), Trubetskaya Street 8, b. 2, Moscow 119991, Russian Federation.
Abstract
AIMS: To examine how standard analytical approaches to model mortality outcomes of alcohol use compare to the true results using the impact of the March 2017 alcohol taxation increase in Lithuania on all-cause mortality as an example. METHODS: Four methodologies were used: two direct methodologies: (a) interrupted time-series on mortality and (b) comparing predictions based on time-series modeling with the real number of deaths for the year following the implementation of the tax increase; and two indirect methodologies: (c) combining a regression-based estimate for the impact of taxation on alcohol consumption with attributable-fraction methodology and (d) using price elasticities from meta-analyses to estimate the impact on alcohol consumption before applying attributable-fraction methodology. RESULTS AND CONCLUSIONS: While all methodologies estimated reductions in all-cause mortality, especially for men, there was substantial variability in the level of mortality reductions predicted. The indirect methodologies had lower predictions as the meta-analyses on elasticities and risk relations seem to underestimate the true values for Lithuania. Directly estimated effects of taxation based on the actual mortalities seem to best represent the true reductions in alcohol-attributable mortality. A significant increase in alcohol excise taxation had a marked impact on all-cause mortality in Lithuania.
AIMS: To examine how standard analytical approaches to model mortality outcomes of alcohol use compare to the true results using the impact of the March 2017 alcohol taxation increase in Lithuania on all-cause mortality as an example. METHODS: Four methodologies were used: two direct methodologies: (a) interrupted time-series on mortality and (b) comparing predictions based on time-series modeling with the real number of deaths for the year following the implementation of the tax increase; and two indirect methodologies: (c) combining a regression-based estimate for the impact of taxation on alcohol consumption with attributable-fraction methodology and (d) using price elasticities from meta-analyses to estimate the impact on alcohol consumption before applying attributable-fraction methodology. RESULTS AND CONCLUSIONS: While all methodologies estimated reductions in all-cause mortality, especially for men, there was substantial variability in the level of mortality reductions predicted. The indirect methodologies had lower predictions as the meta-analyses on elasticities and risk relations seem to underestimate the true values for Lithuania. Directly estimated effects of taxation based on the actual mortalities seem to best represent the true reductions in alcohol-attributable mortality. A significant increase in alcohol excise taxation had a marked impact on all-cause mortality in Lithuania.
Authors: Jürgen Rehm; Urszula Sulkowska; Marta Mańczuk; Paolo Boffetta; John Powles; Svetlana Popova; Witold Zatoński Journal: Int J Epidemiol Date: 2007-01-24 Impact factor: 7.196
Authors: J Rehm; S Marmet; P Anderson; A Gual; L Kraus; D J Nutt; R Room; A V Samokhvalov; E Scafato; M Trapencieris; R W Wiers; G Gmel Journal: Alcohol Alcohol Date: 2013-08-07 Impact factor: 2.826
Authors: Emma Beard; John Marsden; Jamie Brown; Ildiko Tombor; John Stapleton; Susan Michie; Robert West Journal: Addiction Date: 2019-07-09 Impact factor: 6.526
Authors: Kevin Shield; Jakob Manthey; Margaret Rylett; Charlotte Probst; Ashley Wettlaufer; Charles D H Parry; Jürgen Rehm Journal: Lancet Public Health Date: 2020-01
Authors: Jürgen Rehm; Mindaugas Štelemėkas; Carina Ferreira-Borges; Huan Jiang; Shannon Lange; Maria Neufeld; Robin Room; Sally Casswell; Alexander Tran; Jakob Manthey Journal: Int J Environ Res Public Health Date: 2021-03-02 Impact factor: 3.390