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).

Alcohol Use reports an estimated average percent of people who consumed alcohol by type of use and by age range. For the purpose of these data, binge use of alcohol was defined as drinking five or more drinks on the same occasion; i.e. at the same time or within a couple of hours. Dependence is defined consistent with the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) definition as:Spending a lot of time engaging in activities related to substance useUsing a substance in greater quantities or for a longer time than intendedDeveloping tolerance (i.e., needing to use the substance more than before to get desired effects or noticing that the same amount of substance use had less effect than before)Making unsuccessful attempts to cut down on useContinuing substance use despite physical health or emotional problems associated with substance useReducing or eliminating participation in other activities because of substance useExperiencing withdrawal symptoms.Similarly, Abuse is also defined consistent with the DSM-IV definition as the following lifestyle symptoms due to the use of illicit drugs in the past 12 months:Experiencing problems at work, home, and schoolDoing something physically dangerousExperiencing Repeated trouble with the lawExperiencing Problems with family or friends

Analysis of ‘🍷 Alcohol vs Life Expectancy’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/alcohol-vs-life-expectancye on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Credit: This dataset was created by Jonathan Ortiz! All credits for the original go to the original author!

About this dataset

Intro

Findings

There is a surprising relationship between alcohol consumption and life expectancy. In fact, the data suggest that life expectancy and alcohol consumption are positively correlated - 1.2 additional years for every 1 liter of alcohol consumed annually. This is, of course, a spurious finding, because the correlation of this relationship is very low - 0.28. This indicates that other factors in those countries where alcohol consumption is comparatively high or low are contributing to differences in life expectancy, and further analysis is warranted.

https://data.world/api/databeats/dataset/alcohol-vs-life-expectancy/file/raw/LifeExpectancy_v_AlcoholConsumption_Plot.jpg" alt="LifeExpectancy_v_AlcoholConsumption_Plot.jpg">

Methods

1. Addressing Missing Values

The original drinks.csv file in the UNCC/DSBA-6100 dataset was missing values for The Bahamas, Denmark, and Macedonia for the wine, spirits, and beer attributes, respectively. Drinks_solution.csv shows these values filled in, for which I used the Mean of the rest of the data column.

Other methods were considered and ruled out:

• Deleting the Bahamas, Denmark, and Macedonia instances altogether - This is a possible route, but the data file itself is just under 200 rows, and there is only one observation for each country. Because the dataset is relatively small by number of instances, removal should be avoided in order to give the model more data to use.
• Imputing missing values with k-Nearest Neighbors - Another possible route, knn impute can yield higher accuracy in certain cases when the dataset is fairly large. However, this particular dataset only contains 3 attributes, all of which seem unrelated to each other. If we had more columns with more data like availability, annual sales, preferences, etc. of the different drinks, it would be possible to predict these values with knn, but this approach should be avoided given the data we have.
• Filling missing values with a MODE - By visualizing the data, it is easy to see that each column is fairly skewed, with many countries reporting 0 in one or more of the servings columns. Using the MODE would fill these missing entries with 0 for all three (beer_servings, spirit_servings, and wine_servings), and upon reviewing the Bahamas, Denmark, and Macedonia more closely, it is apparent that 0 would be a poor choice for the missing values, as all three countries clearly consume alcohol.
• Filling missing values with MEDIAN - Due to the skewness mentioned just above in the MODE section, using a MEDIAN of the whole column would also be a poor choice, as the MEDIAN is pulled down by several countries reporting 0 or 1. A MEDIAN of only the observations reporting 1 or more servings--or another cutoff--could be used, however, and this would be acceptable.

Filling missing values with MEAN - In the case of the drinks dataset, this is the best approach. The MEAN averages for the columns happen to be very close to the actual data from where we sourced this exercise. In addition, the MEAN will not skew the data, which the prior approaches would do.

2. Calculating New Attributes

The original drinks.csv dataset also had an empty data column: total_litres_of_pure_alcohol. This column needed to be calculated in order to do a simple 2D plot and trendline. It would have been possible to instead run a multi-variable regression on the data and therefore skip this step, but this adds an extra layer of complication to understanding the analysis - not to mention the point of the exercise is to go through an example of calculating new attributes (or "feature engineering") using domain knowledge.

The graphic found at the Wikipedia / Standard Drink page shows the following breakdown:

• Beer - 12 fl oz per serving - 5% average ABV
• Wing - 5 fl oz - 12% ABV
• Spirits - 1.5 fl oz - 40% ABV

The conversion factor from fl oz to L is 1 fl oz : 0.0295735 L

Therefore, the following formula was used to compute the empty column:
total_litres_of_pure_alcohol
=
(beer_servings * 12 fl oz per serving * 0.05 ABV + spirit_servings * 1.5 fl oz * 0.4 ABV + wine_servings * 5 fl oz * 0.12 ABV) * 0.0295735 liters per fl oz

3. Joining To External Data

The lifeexpectancy.csv datafile in the https://data.world/uncc-dsba/dsba-6100-fall-2016 dataset contains life expectancy data for each country. The following query will join this data to the cleaned drinks.csv data file:

# Life Expectancy vs Alcohol Consumption

Life Expectancy vs. Alcohol Consumption with countryTable

PREFIX drinks: <http://data.world/databeats/alcohol-vs-life-expectancy/drinks_solution.csv/drinks_solution#>
PREFIX life: <http://data.world/uncc-dsba/dsba-6100-fall-2016/lifeexpectancy.csv/lifeexpectancy#>
PREFIX countries: <http://data.world/databeats/alcohol-vs-life-expectancy/countryTable.csv/countryTable#>

SELECT ?country ?alc ?years
WHERE {
  SERVICE <https://query.data.world/sparql/databeats/alcohol-vs-life-expectancy> {
    ?r1 drinks:total_litres_of_pure_alcohol ?alc .
    ?r1 drinks:country ?country .
    ?r2 countries:drinksCountry ?country .
    ?r2 countries:leCountry ?leCountry .
  }

  SERVICE <https://query.data.world/sparql/uncc-dsba/dsba-6100-fall-2016> {
    ?r3 life:CountryDisplay ?leCountry .
    ?r3 life:GhoCode ?gho_code .
    ?r3 life:Numeric ?years .
    ?r3 life:YearCode ?reporting_year .
    ?r3 life:SexDisplay ?sex .
  }
FILTER ( ?gho_code = "WHOSIS_000001" && ?reporting_year = 2013 && ?sex = "Both sexes" )
}
ORDER BY ?country

4. Plotting

The resulting joined data can then be saved to local disk and imported into any analysis tool like Excel, Numbers, R, etc. to make a simple scatterplot. A trendline and R^2 should be added to determine the relationship between Alcohol Consumption and Life Expectancy (if any).

https://data.world/api/databeats/dataset/alcohol-vs-life-expectancy/file/raw/LifeExpectancy_v_AlcoholConsumption_Plot.jpg" alt="LifeExpectancy_v_AlcoholConsumption_Plot.jpg">

This dataset was created by Jonathan Ortiz and contains around 200 samples along with Beer Servings, Spirit Servings, technical information and other features such as: - Total Litres Of Pure Alcohol - Wine Servings - and more.

How to use this dataset

• Analyze Beer Servings in relation to Spirit Servings
• Study the influence of Total Litres Of Pure Alcohol on Wine Servings
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Jonathan Ortiz

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Alcohol Consumption

This folder contains the data behind the story Dear Mona Followup: Where Do People Drink The Most Beer, Wine And Spirits?

Units: Average serving sizes per person Source: World Health Organisation, Global Information System on Alcohol and Health (GISAH), 2010

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).

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.

The prevalence of the metabolic syndrome is rising worldwide. Its association with alcohol intake, a major lifestyle factor, is unclear, particularly with respect to the influence of drinking with as opposed to outside of meals. We investigated the associations of different aspects of alcohol consumption with the metabolic syndrome and its components. In cross-sectional analyses of 14,375 active or retired civil servants (aged 35–74 years) participating in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), we fitted logistic regression models to investigate interactions between the quantity of alcohol, the timing of its consumption with respect to meals, and the predominant beverage type in the association of alcohol consumption with the metabolic syndrome. In analyses adjusted for age, sex, educational level, income, socioeconomic status, ethnicity, smoking, body mass index, and physical activity, light consumption of alcoholic beverages with meals was inversely associated with the metabolic syndrome (≤4 drinks/week: OR = 0.85, 95%CI 0.74–0.97; 4 to 7 drinks/week: OR = 0.75, 95%CI 0.61–0.92), compared to abstention/occasional drinking. On the other hand, greater consumption of alcohol consumed outside of meals was significantly associated with the metabolic syndrome (7 to 14 drinks/week: OR = 1.32, 95%CI 1.11–1.57; ≥14 drinks/week: OR = 1.60, 95%CI 1.29–1.98). Drinking predominantly wine, which occurred mostly with meals, was significantly related to a lower syndrome prevalence; drinking predominantly beer, most notably when outside of meals and in larger quantity, was frequently associated with a greater prevalence. In conclusion, the alcohol—metabolic syndrome association differs markedly depending on the relationship of intake to meals. Beverage preference—wine or beer—appears to underlie at least part of this difference. Notably, most alcohol was consumed in metabolically unfavorable type and timing. If further investigations extend these findings to clinically relevant endpoints, public policies should recommend that alcohol, when taken, should be preferably consumed with meals.

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.

Global Distilled Alcoholic Beverages Market Size Volume Per Capita by Country, 2023 Discover more data with ReportLinker!

Global Alcoholic Beverages Production Share by Country (Thousand Metric Tons), 2023 Discover more data with ReportLinker!

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."

Since the 1950s, the consumption of alcoholic beverages has changed very considerably around the world. In high-income countries, consumers tended to drink mostly what could be best produced domestically (spirits in the cold north, wine in temperate climates, and beer in countries too cold for winegrapes yet warm enough to grow malting barley). With increasing globalization and interactions between cultures, however, countries are converging in their beverage consumption patterns. In emerging economies, meanwhile, much of their alcohol was produced at home and not recorded, but that too is changing with their urbanization and income growth. This new database covers all countries of the world, introduces two new summary indicators to capture the extent of convergence in national alcohol consumption levels and in their mix of beverages, and distinguishes countries according to whether their alcoholic focus was on wine, beer or spirits in the early 1960s as well as their geographic region and their real per capita income. For recent decades expenditure data are included and we compare alcohol with soft drink retail expenditure, and show what difference it makes when WHO estimates of unrecorded alcohol volumes are included as part of total alcohol consumption. A report summarizing the data is available as Wine Economics Research Centre Working Paper 0117.

The data are in three Excel files that are freely downloadable below. Please acknowledge the source as: Holmes, A.J. and K. Anderson, Annual Database of National Beverage Consumption Volumes and Expenditures, 1950 to 2015. Wine Economics Research Centre, University of Adelaide

Data for cities, communities, and City of Los Angeles Council Districts were generated using a small area estimation method which combined the survey data with population benchmark data (2022 population estimates for Los Angeles County) and neighborhood characteristics data (e.g., U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates). This indicator is based on self-report and includes adults who had at least one drink of any alcoholic beverage (such as beer, wine, or liquor) in the past month.In the US, alcohol use is legal for those ages 21 years and older and should be avoided or used in moderation (defined as consuming two or less drinks per day for men or one or less drinks per day for women). Excessive alcohol use includes binge drinking, heavy drinking, any underage alcohol use, and any alcohol use by pregnant persons. Alcohol use is associated with numerous health, safety, and social problems, including chronic diseases, unintentional injuries, interpersonal violence, fetal alcohol spectrum disorders, alcohol use disorders, and weakened interpersonal relationships and ability to function at work, school, or home. In general, people with higher socioeconomic status (SES) report drinking more frequently and more heavily than those with lower SES; however, people with lower SES are on average more negatively affected by alcohol-related harms. It is important for cities and communities to build strategies that create environments that reduce excessive alcohol use and prevent underage drinking.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

This dataset charted Finnish consumption of alcoholic beverages in terms of individual drinking occasions. The data were collected as part of the Finnish Drinking Habits Survey 2016 (main data: FSD3282). FSD's holdings also include a dataset belonging to the same study concerning abstaining from drinking during occasions where other people consumed alcohol (FSD3314). The study examined situations in which the respondents had consumed alcoholic beverages: how many centilitres they had consumed of different alcoholic drinks, where, when and with whom. The respondents could provide information on multiple drinking occasions, and the same questions were asked about each of them. The data also contain conversions made from variables in the questionnaire, e.g. conversions of consumed quantities of different drinks into pure alcohol. The questionnaire (in Finnish) describes in more detail the coefficients used in the conversions as well as the formula for calculating the respondents' estimated blood-alcohol content (per mille) during each drinking occasion. Background variables include gender, age, date and weekday of the drinking occasion as well as starting and ending times for drinking.

This folder contains the data behind the story Dear Mona Followup: Where Do People Drink The Most Beer, Wine And Spirits?

Units: Average serving sizes per person Source: World Health Organisation, Gl...

Sales and per capita sales of alcoholic beverages by liquor authorities and other retail outlets, by value, volume, and absolute volume, annual, 5 most recent time periods.

This dataset contains longitudinal data (3 waves) of both parents and their children (n=329), focusing on alcohol use (parents) and alcohol-related cognitions (children). Evidence suggests an association between perceived alcohol-related norms and personal consumption. These perceptions develop over years of observation and exposure to alcohol, likely beginning in early childhood, and likely differing by sex. Understanding the early development of perceptions of drinking may provide insight into the development of gendered drinking practices. The aim of this study is to explore boys’ and girls’ perceptions about men and women’s alcohol consumption, and whether and how these change over time as children age. 329 children (aged four to six years at baseline) completed the Dutch electronic Appropriate Beverage Task annually for three consecutive years (2015 [baseline], 2016, 2017). Regression models were used to examine whether perceptions of consumption varied as a function of the gender of the adult, participants’ sex, and any changes over time. In illustrated pictures, children perceived men (39%) drank alcoholic beverages more often than women (24%). Men were perceived to drink alcohol more frequently than women at baseline and this difference increased with age. Girls were more likely to perceive men drinking at baseline (aged 4-6), but there were little sex differences by time point three (aged 6-8). From a young age children perceive that men drink more than women. These perceptions strengthen as children grow older, with young girls perceiving these gender differences at earlier ages than boys. Understanding children’s perceptions of gendered drinking norms and their development over time can enable targeted prevention efforts.

1) Data Introduction • The Smoking and Drinking Dataset with body signal Dataset is a structured medical dataset compiled from health examination records collected by the National Health Insurance Service (NHIS) of South Korea. It includes demographic information such as age and gender, as well as physiological indicators like blood pressure, vision, hearing, and various blood test metrics.

2) Data Utilization (1) Characteristics of the Smoking and Drinking Dataset with body signal Dataset: • The dataset contains over 22 columns, including variables such as gender, age, height, weight, blood pressure, fasting blood glucose, cholesterol levels, liver function markers (SGOT/AST, ALT, gamma-GTP), and serum creatinine, among other physiological and clinical indicators.

(2) Applications of the Smoking and Drinking Dataset with body signal Dataset: • Alcohol Consumption Prediction Model Training: This dataset can be used to train machine learning classification models that predict an individual’s drinking status based on physical and blood health indicators. • Behavioral Analysis Based on Health Indicators: It enables analysis of correlations between physiological signals and drinking behavior, identification of high-risk groups, and development of lifestyle-based health evaluation systems.

Global Alcoholic Beverages Market Size Volume Per Capita by Country, 2023 Discover more data with ReportLinker!

Abstract (en): This survey focused on alcohol use and alcohol problems among undergraduate college students. The survey collected information on students' use of alcohol, tobacco, and illicit drugs, views on campus alcohol policies and student alcohol use, reasons for drinking alcohol and reasons for not drinking or limiting drinking, and personal difficulties caused by drinking problems (e.g., missed classes, injury, and trouble with police). Additional topics covered by the survey include overall health status, daily activities, satisfaction with education being received, grade-point average, living arrangements, social life, sexual activity, use of condoms during sexual intercourse, date rape, drunk driving, and attendance in meetings of Alcoholics Anonymous, Alanon, Adult Children of Alcoholics, and Narcotics Anonymous. Background variables include age, height, weight, sex, marital status, religion, mother's and father's education, mother's and father's drinking habits, race, and Hispanic origin. Datasets:DS1: Dataset Full-time undergraduate students enrolled in four-year colleges or universities in the United States. A random sample of students in 140 schools was selected using probability proportionate to size sampling of colleges and universities. However, the data only include the 119 schools in the analytic sample of the 2001 round of the Harvard School of Public Health College Alcohol Study (ICPSR 4291). Of the 120 schools surveyed for the 2001 round, 119 met the response rate cutoff for inclusion in that year's analytical sample. The last ICPSR version of this study (i.e., the second version of ICPSR 6577) only included the 119 schools of the 1999 analytical sample (ICPSR 3818), which were slightly different from the 119 schools in the 2001 analytical sample. In 1999, 119 of the 128 schools surveyed met the response rate cutoff for inclusion in the 1999 analytical sample. 2020-01-30 Online variable search capabilities have been added for this study.2005-11-14. The study has been updated with revised data supplied by the principal investigator to allow for comparable analyses with the Harvard School of Public Health College Alcohol Study, 2001 (ICPSR 4291). This version of the data comprises the same 119 schools in the ICPSR 4291 data. Six schools in the previous version were dropped, six schools not in the previous version were added, and seven variables (MMDD, SERIAL, NEWC19_A, NEWC19_B, NEWC19_C, NEWC19_D, and WAVE) were added. In addition, ICPSR revised the documentation accordingly and added a SAS setup file to the collection. The SAS setup includes the PROC FORMAT statements from the previous version.2003-12-02 The principal investigator supplied a revised data file in which the number of colleges was reduced from 140 to 119 (2,189 cases were dropped). Some variables were removed, others were renamed, and new variables, including a weight variable, were added. In addition, ICPSR has identified 11 variables with one or more revised values (variables DRIVE, NUMPROB, PROBGULP, PROB5, PERSIST, NOBINGE, UPTAKE, GIVEUP, CONTBING, ANYDRUG1, and ANYDRUG2). The documentation and other supporting files were revised accordingly. Funding institution(s): Robert Wood Johnson Foundation (19547).