Our findings highlight the need for individuals and policymakers to make informed decisions to mitigate the impact of high-risk alcohol drinking in the United States. ConclusionsĪ short-term increase in alcohol consumption during the COVID-19 pandemic can substantially increase long-term ALD-related morbidity and mortality. A sustained increase in alcohol consumption for more than 1 year could result in additional morbidity and mortality. Between 20, alcohol consumption changes due to COVID-19 will lead to 100 (100–200) additional deaths and 2800 (2700–2900) additional decompensated cirrhosis cases. One-year increase in alcohol consumption during the COVID-19 pandemic is estimated to result in 8000 (95% uncertainty interval, 7500–8600) additional ALD-related deaths, 18,700 (95% UI, 17,600–19,900) cases of decompensated cirrhosis, and 1000 (95% UI, 1000–1100) cases of HCC, and 8.9 million disability-adjusted life years between 20. We compared these outcomes with a counterfactual scenario wherein no COVID-19 occurs and drinking patterns do not change. We modeled short- and long-term outcomes of current drinking patterns during COVID-19 (status quo) using survey data of changes in alcohol consumption in a nationally representative sample between February and November 2020. We extended a previously validated microsimulation model that estimated the short- and long-term effect of increased drinking during the COVID-19 pandemic in individuals in the United States born between 19. We projected the effect of increased alcohol consumption on alcohol-associated liver disease (ALD) and mortality. U.S.Alcohol consumption increased during the COVID-19 pandemic in 2020 in the United States. Litres of alcohol consumed per capita in Denmark 2003-2013Īlcohol-related emergency department visits in the U.S. Liters of alcohol consumed per capita in Italy 2003-2010 2006-2010Ĭonsumption of alcohol among drinkers in Malaysia 2016 by genderĪlcohol abstainers in the U.S. Liters of alcohol consumed per capita in Czechia 2005-2014Īnnual deaths due to to alcohol abuse in the U.S. Liters of alcohol consumed per capita in Hungary 2003-2012 Liters of alcohol consumed per capita in Portugal 2003-2011 Liters of alcohol consumed per capita in Romania 2003-2011 School children: type of alcohol consumed in the last week in England 2018, by gender Future years are mostly Statista projections These projections or forecasts are conducted by regression analyses, exponential trend smoothing (ETS) or similar techniques and extrapolate the found historical trend. As new data becomes available or methodologies are adapted to suit changing requirements it can be possible that data is not comparable any longer with previously published data or is changed retroactively according to the new definitions.īecause of the high degree of processing no specific external source can be named for each data point and all data for historical years (usually until the last finished year before the current one) have to be considered Statista estimates. Most indicators are composites of multiple input sources with slightly varying methodologies that have been processed by our analysts to be aligned and consistent with each other and with all other indicators in the KMI database. Data for missing countries or regions are imputed by considering known information from other countries or regions that are found to be similar by cluster analyses like k-means or similar procedures. Data for missing years are interpolated by various statistical means, such as linear or exponential interpolation or cubic splines. These datasets are often incomplete as there are gaps between survey years or no or no reliable information might be available for a specific indicator in a specific country or region. Whereas primary data are generated via Statista's own surveys like the Global Consumer Survey, secondary input datasets are mostly sourced from international institutions (such as the IMF, the World Bank or the United Nations), national statistical offices, trade associations and from the trade press. The shown forecasts represent a blend of multiple input datasets from both internal (primary) and external (secondary) sources.
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