Methods for estimating avoidable costs of excessive alcohol consumption

Abstract

Background: Alcohol is a risk factor with serious consequences for society and individuals. This study aims to present methods and approaches that might be used to estimate the costs related to excessive alcohol consumption. It emphasizes the need for general methods and approaches that are easily applicable, because the level of digitalization and data availability vary across regions. The lack of data makes many methods inapplicable and useless. The ease of applicability will help to make cost-of-illness studies and their results comparable globally. Methods: This study is based on data from the Czech Republic in 2017. Drinking alcohol results in costs of healthcare, social care, law enforcement, and administrative costs of public authorities. To quantify the cost of drinking in the Czech Republic, the top-down approach, bottom-up approach, human capital approach and attributable fractions were used. Results: In 2017, the cost related to alcohol was estimated at 0.66% of the national GDP. Lost productivity represented 54.45% of total cost related to alcohol. All cost related to alcohol is considered to be avoidable. Conclusions: The methods and approaches applied to estimate the cost of disease or any other health issue should be generalized regarding the availability of data and specifics of provided services to people who are addicted or have any kind of disability. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Slovak Research and Development AgencySlovak Research and Development Agency [APVV-17-0360]; Government Council for Drug Policy Coordination of the Czech Republic [A-86-19]; Internal Grant Agency of FaME Tomas Bata University in Zlin: RVO/2020: "Economic quantification of marketing processes that focus on value increase for a patient in a process of system creation to measure and control efficiency in health facilities in theAgentúra na Podporu Výskumu a Vývoja, APVV: APVV-17-0360; Univerzita Tomáše Bati ve Zlín