Annual data for Great Britain on teetotalism, drinking in the week before survey interview, frequent drinking and units drunk, including analysis by sex, age and socioeconomic status.

This report uses 2011 to 2012 National Survey on Drug Use and Health (NSDUH) to assess past month alcohol use and binge alcohol use among pregnant women aged 15 to 44 by trimester of pregnancy.

This layer represents the Percent of Adults who Binge Drink calculated from the 2014-2017 Colorado Behavioral Risk Factor Surveillance System (County or Regional Estimates) data set. These data represent the estimated prevalence of Binge Drinking among adults (Age 18+) for each county in Colorado. Binge Drinking is defined for males as having five or more drinks on one occasion and for females as having four or more drinks on one occasion within the past 30 days. Binge Drinking is calculated from the number of days alcohol was consumed in the past 30 days, and the average number of drinks consumed on those days. Regional estimates were used if there was not enough sample size to calculate a single county estimate. The estimate for each county was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).

Ratio: Percent of live births whose mothers drank alcohol during pregnancy.

Definition: Self-reported use of alcohol by the mother during pregnancy. Denominator includes mothers with unknown alcohol use status (approx. 5% of birth records).

Source: Birth Certificate Database, Office of Vital Statistics and Registry, New Jersey Department of Health

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This dataset provides alcohol use prevalence estimates for any drinking by county, year, and sex for all states and counties, the District of Columbia, and the US as a whole for 2002-2012. "Any" drinking defined as at least one drink of any alcoholic beverage in the past 30 days. The data also include changes by percent for the period.

This dataset tracks the updates made on the dataset "18 Percent of Pregnant Women Drink Alcohol during Early Pregnancy (2011 to 2012 NSDUH)" as a repository for previous versions of the data and metadata.

This layer represents the Percent of Adults who Drink Heavily calculated from the 2014-2017 Colorado Behavioral Risk Factor Surveillance System (County or Regional Estimates) data set. These data represent the estimated prevalence of Heavy Drinking among adults (Age 18+) for each county in Colorado. Heavy Drinking is defined for males as having 15 or more drinks per week and for females as having 8 or more drinks per week. Heavy Drinking is calculated from the number of days alcohol was consumed in the past 30 days, and the average number of drinks consumed on those days. Regional estimates were used if there was not enough sample size to calculate a single county estimate. The estimate for each county was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).

Beer remains the greatest source of per capita alcohol consumption in the United States, and increasing market availability and consumer demand for higher alcohol has meaningful public health consequences. Objectives: To determine whether apparent alcohol intake from beer changed among households over time, we used nationally-representative US Nielsen Consumer Panel purchasing data from 2004 to 2014, and incorporated information on percent alcohol by volume (ABV) to compute the number of standard drinks of alcohol consumed from beer as a result. Methods: We queried external data sources (e.g. official manufacture, consumer beer-related websites) to obtain beer-specific ABVs, merged this information with Nielsen consumer-level data, and calculated the average rate of beer and standard drink consumption per household per year. We used joinpoint regression to estimate annual percentage changes and annual absolute changes in intake over time, with separate piecewise linear segments fit between years if a significant deviation in trend was detected. Results: Higher alcohol content beer consumption increased steadily across the decade, accounting for 9.6% of total intake in 2004 compared to 21.6% of total intake by 2014. Standard drink intake from beer declined sharply post-2011 by 3.04% annually (95% CI: −5.93, −0.06) or by 4.52 standard drinks (95% CI: −8.69, −0.35) yearly – coinciding with several beer industry transitions, market share fluctuations, and consumer preference changes for beer occurring around that time. Conclusions: Despite consistent increases in higher alcohol content beer intake across the decade, households do not appear to be consuming more standard drinks of alcohol from beer as a result. Supplemental data for this article is available online at https://doi.org/10.1080/10826084.2021.1928208 .

This dataset presents the footprint of the crude percentage of adults who consume more than two standard drinks per day on average. Lifetime risky alcohol consumption is defined as those adults who consume more than two standard drinks per day on average, thereby increasing their lifetime risk. As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the Australian Bureau of Statistics (ABS) using standard error estimates of the proportion. The data spans the financial year of 2014-2015 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). Health risk factors are attributes, characteristics or exposures that increase the likelihood of a person developing a disease or health disorder. Examples of health risk factors include risky alcohol consumption, physical inactivity and high blood pressure. High-quality information on health risk factors is important in providing an evidence base to inform health policy, program and service delivery. For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Health Risk Factors in 2014-2015 Data Tables. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. The health risks factors reported are known to vary with age and the different PHN area populations are known to have a range of age structures. As such, comparisons of results between the PHN areas should be made with caution because the crude rates presented do not account for these age differences. Adults are defined as persons aged 18 years and over. Values assigned to "n.p." in the original data have been removed from the data.

Alcohol consumption in India amounted to about *****billion liters in 2020 and was estimated to reach about **** billion liters by 2024. The increase in the consumption of these beverages can be attributed to multiple factors, including the rising levels of disposable income and a growing urban population, among others.   Alcohol market in India India’s alcohol market consisted of two main kinds of liquor – Indian made Indian liquor or IMIL, and Indian made foreign liquor or IMFL. This was in addition to beer, wine, and other imported alcohol. Country liquor accounted for the highest market share, while spirits took up the majority of the consumption market. Young consumers Although the average per-adult intake of alcohol was considerably lower in India when compared to other countries such as the United States, heavy drinkers among young Indians were more prevalent. Men were more likely to drink than women by a large margin and were also more prone to episodic drinking. According to a study, over ** percent of Indians aged under 25 purchase or consume alcoholic beverages even though it is illegal. This was despite bans on alcohol in some states across the country and limitations on sales in some others.

This feature layer represents Sustainable Development Goal indicator 3.5.2 'Prevalence of Drinking Alcohol as Percentage of the Population' for Ireland. This layer was created using Irish Health Survey 2015 data produced by Central Statistics Office (CSO) and NUTS 3 boundary data produced by Tailte Éireann. Note that the NUTS 3 boundary refers to the former Regional Authorities established under the NUTS Regulation (Regulation (EU) 1059/2003). These boundaries were subsequently revised in 2016 through Commission Regulation (EU) 2016/2066 amending annexes to Regulation 1059/2003 (more info).

In 2015 UN countries adopted a set of 17 goals to end poverty, protect the planet and ensure prosperity for all as part of a new sustainable development agenda. Each goal has specific targets to help achieve the goals set out in the agenda by 2030. Governments are committed to establishing national frameworks for the achievement of the 17 Goals and to review progress using accessible quality data. With these goals in mind the CSO and Tailte Éireann are working together to link geography and statistics to produce indicators that help communicate and monitor Ireland’s performance in relation to achieving the 17 sustainable development goals.The indicator displayed supports the efforts to achieve goal number 3 which aims to ensure healthy lives and promote well-being for all at all ages.

The alcohol drinking status and demographic characteristics in Iran, 2016 (National Level).

This collection focuses on how changes in the legal drinking age affect the number of fatal motor vehicle accidents and crime rates. The principal investigators identified three areas of study. First, they looked at blood alcohol content of drivers involved in fatal accidents in relation to changes in the drinking age. Second, they looked at how arrest rates correlated with changes in the drinking age. Finally, they looked at the relationship between blood alcohol content and arrest rates. In this context, the investigators used the percentage of drivers killed in fatal automobile accidents who had positive blood alcohol content as an indicator of drinking in the population. Arrests were used as a measure of crime, and arrest rates per capita were used to create comparability across states and over time. Arrests for certain crimes as a proportion of all arrests were used for other analyses to compensate for trends that affect the probability of arrests in general. This collection contains three parts. Variables in the Federal Bureau of Investigation Crime Data file (Part 1) include the state and year to which the data apply, the type of crime, and the sex and age category of those arrested for crimes. A single arrest is the unit of analysis for this file. Information in the Population Data file (Part 2) includes population counts for the number of individuals within each of seven age categories, as well as the number in the total population. There is also a figure for the number of individuals covered by the reporting police agencies from which data were gathered. The individual is the unit of analysis. The Fatal Accident Data file (Part 3) includes six variables: the FIPS code for the state, year of accident, and the sex, age group, and blood alcohol content of the individual killed. The final variable in each record is a count of the number of drivers killed in fatal motor vehicle accidents for that state and year who fit into the given sex, age, and blood alcohol content grouping. A driver killed in a fatal accident is the unit of analysis.

The alcohol consumption per capita ranking is led by Romania with ***** liters, while Georgia is following with ***** liters. In contrast, Bangladesh is at the bottom of the ranking with **** liters, showing a difference of ***** liters to Romania. Depicted is the estimated alcohol consumption in the country or region at hand.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

These data represent the predicted (modeled) prevalence of Heavy Drinking among adults (Age 18+) for each census tract in Colorado. Heavy Drinking is defined for males as having 15 or more drinks per week and for females as having 8 or more drinks per week. Heavy Drinking is calculated from the number of days alcohol was consumed in the past 30 days, and the average number of drinks consumed on those days.The estimate for each census tract represents an average that was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).CDPHE used a model-based approach to measure the relationship between age, race, gender, poverty, education, location and health conditions or risk behavior indicators and applied this relationship to predict the number of persons' who have the health conditions or risk behavior for each census tract in Colorado. We then applied these probabilities, based on demographic stratification, to the 2013-2017 American Community Survey population estimates and determined the percentage of adults with the health conditions or risk behavior for each census tract in Colorado.The estimates are based on statistical models and are not direct survey estimates. Using the best available data, CDPHE was able to model census tract estimates based on demographic data and background knowledge about the distribution of specific health conditions and risk behaviors.The estimates are displayed in both the map and data table using point estimate values for each census tract and displayed using a Quintile range. The high and low value for each color on the map is calculated based on dividing the total number of census tracts in Colorado (1249) into five groups based on the total range of estimates for all Colorado census tracts. Each Quintile range represents roughly 20% of the census tracts in Colorado. No estimates are provided for census tracts with a known population of less than 50. These census tracts are displayed in the map as "No Est, Pop < 50."No estimates are provided for 7 census tracts with a known population of less than 50 or for the 2 census tracts that exclusively contain a federal correctional institution as 100% of their population. These 9 census tracts are displayed in the map as "No Estimate."

The alcohol consumption per capita in Mexico was forecast to continuously increase between 2024 and 2029 by in total *** liters (+**** percent). After the ninth consecutive increasing year, the per capita consumption is estimated to reach **** liters and therefore a new peak in 2029. Depicted is the estimated alcohol consumption in the country or region at hand.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the alcohol consumption per capita in countries like Canada and United States.

This report contains results from the latest survey of secondary school pupils in England in years 7 to 11 (mostly aged 11 to 15), focusing on smoking, drinking and drug use. It covers a range of topics including prevalence, habits, attitudes, and wellbeing. This survey is usually run every two years, however, due to the impact that the Covid pandemic had on school opening and attendance, it was not possible to run the survey as initially planned in 2020; instead it was delivered in the 2021 school year. In 2021 additional questions were also included relating to the impact of Covid. They covered how pupil's took part in school learning in the last school year (September 2020 to July 2021), and how often pupil's met other people outside of school and home. Results of analysis covering these questions have been presented within parts of the report and associated data tables. It includes this summary report showing key findings, excel tables with more detailed outcomes, technical appendices and a data quality statement. An anonymised record level file of the underlying data on which users can carry out their own analysis will be made available via the UK Data Service later in 2022 (see link below).

Argentina. EnPreCosp survey 2011.

These data represent the predicted (modeled) prevalence of Binge Drinking among adults (Age 18+) for each census tract in Colorado. Binge Drinking is defined for males as having five or more drinks on one occasion and for females as having four or more drinks on one occasion within the past 30 days. Binge Drinking is calculated from the number of days alcohol was consumed in the past 30 days, and the average number of drinks consumed on those days.The estimate for each census tract represents an average that was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).CDPHE used a model-based approach to measure the relationship between age, race, gender, poverty, education, location and health conditions or risk behavior indicators and applied this relationship to predict the number of persons' who have the health conditions or risk behavior for each census tract in Colorado. We then applied these probabilities, based on demographic stratification, to the 2013-2017 American Community Survey population estimates and determined the percentage of adults with the health conditions or risk behavior for each census tract in Colorado.The estimates are based on statistical models and are not direct survey estimates. Using the best available data, CDPHE was able to model census tract estimates based on demographic data and background knowledge about the distribution of specific health conditions and risk behaviors.The estimates are displayed in both the map and data table using point estimate values for each census tract and displayed using a Quintile range. The high and low value for each color on the map is calculated based on dividing the total number of census tracts in Colorado (1249) into five groups based on the total range of estimates for all Colorado census tracts. Each Quintile range represents roughly 20% of the census tracts in Colorado. No estimates are provided for census tracts with a known population of less than 50. These census tracts are displayed in the map as "No Est, Pop < 50."No estimates are provided for 7 census tracts with a known population of less than 50 or for the 2 census tracts that exclusively contain a federal correctional institution as 100% of their population. These 9 census tracts are displayed in the map as "No Estimate."

Contains a set of data tables for each part of the Smoking, Drinking and Drug Use among Young People in England, 2021 report