Annual data on age-standardised and age-specific alcohol-specific death rates in the UK, its constituent countries and regions of England.

Alcohol and Life Expectancy

An International Study

By Jon Loyens [source]

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How to use the dataset

The drinks table provides information on the beer, spirit, and wine servings per capita in various countries as well as their total litres of pure alcohol consumed per person. The lifeexpectancy-verbose table includes a wider range of variables such as GhoCode, GhoDisplay, PublishStateCode etc. but most importantly include sex-specific Numeric values which indicate a person’s remaining life expectancy in years at birth for that particular year given their gender/sex at that time from a specific region or Country (if available).

In order to utilize this dataset for research effectively it is important to have an understanding on how these tables are connected with each other through common columns like RegionDisplay which is present in both tables which can be used to match the corresponding items present across different sources for comparison purposes. Besides this one can also look into further insights based upon other columns like CountryCode & WorldBankIncomeGroupGroupCode etc depending upon their research topic.

Once you have understood what variables need to be compared against each other between both these tables it becomes easy to use them together using different methods like linking multiple Excel sheets together or writing queries using SQLite or Python scripts if a larger scale comparison needs to be done or simply creating scatterplots using tools like Tableau etc., so that relationships between drinking habits & mortality rates can be visually investigated more effectively as well meaningfully make interpretations out of correlations observed within this dataset

Research Ideas

• Examining the relationship between income group and total litres of pure alcohol consumption
• Analyzing the correlation between life expectancy and beer, spirit, and wine servings
• Understanding the differences in total litres of pure alcohol consumption across regions, countries, sexes and years

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: lifeexpectancy-verbose.csv | Column name | Description | |:----------------------------------|:------------------------------------------------| | GhoCode | Global Health Observatory code (String) | | GhoDisplay | Global Health Observatory display name (String) | | PublishStateCode | Publish state code (String) | | PublishStateDisplay | Publish state display name (String) | | YearCode | Year code (Integer) | | YearDisplay | Year display name (String) | | RegionCode | Region code (String) | | RegionDisplay | Region display name (String) | | WorldBankIncomeGroupGroupCode | World Bank Income Group Group Code (String) | | WorldBankIncomeGroupDisplay | World Bank Income Group Display Name (String) | | CountryCode | Country code (String) | | CountryDisplay | Country display name (String) | | SexCode | Sex code (String) | | SexDisplay | Sex display name (String) | | DisplayValue | Display value (String) | | Numeric | Numeric value (Float) |

File: drinks.csv | Column name ...

Annual data on number of deaths, age-standardised death rates and median registration delays for local authorities in England and Wales.

Alcohol Consumption by State 2024

Columns:

• State Name: names of the states of the USA.
• State Abbreviations (USPS): the United States Postal Service (USPS) abbreviations for each state. These two-letter codes are commonly used to represent states in various contexts.
• Gallons of Ethanol per Capita (Gallons Consumed): amount of ethanol consumed per capita in each state, measured in gallons.
• Driving Fatalities Involving Alcohol (Percentage): the percentage of driving fatalities in each state that involve alcohol.
• Excessive Drinking Rate (Percentage): the percentage of the population engaging in excessive drinking behaviors in each state. Excessive alcohol consumption includes heavy drinking and binge drinking. Heavy drinking is eight or more drinks per week for women and 15 or more drinks per week for men. Binge drinking is four or more drinks during a single occasion for women and five or more for men

Context

This dataset contains total per capita (15+ years) alcohol consumption (in liters of pure alcohol) collected by WHO for 2000-2019 (Indicator ID 465).

If you want to take a look at the more detailed consumption, aggregated by beverage type, I'd suggest taking a look either in one of other Kaggle datasets or WHO's alcohol, recorded per capita (15+) consumption indicator.

This dataset was necessary for my current project I'm working on which concerns suicide rates and different factors correlating with them.

Content

The data is broken up by geographic region, country, year and sex. The period covered is 2000-2019.

The total alcohol per capita consumption (APC) comprises both the recorded and the unrecorded APC, which together provide a more accurate estimate of the level of alcohol consumption in a country, and as a result, portray trends of alcohol consumption in the adult population (15 years of age and older) in a more precise way. Drinking alcohol can associated with developing alcohol use disorder or dependence and higher risk of mental and behavioral disorders. It is a major risk for liver cirrhosis, some cancers and cardiovascular diseases as well as injuries resulting from violence and accidents. Beyond health consequences, the harmful use of alcohol brings significant social and economic losses to individuals, their families and society at large.

Total APC is defined as the total (sum of three-year average recorded and three-year average unrecorded APC, adjusted for three-year average tourist consumption) amount of alcohol consumed per adult (15+ years) over a calendar year, in liters of pure alcohol. Recorded alcohol consumption refers to official statistics (production, import, export, and sales or taxation data), while the unrecorded alcohol consumption refers to alcohol which is not taxed and is outside the usual system of governmental control. Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries. Positive figures denote alcohol consumption of outbound tourists being greater than alcohol consumption by inbound tourists, negative numbers the opposite. Tourist consumption is based on UN tourist statistics.

Acknowledgements

The data is obtained from the World Health Organization Global Health Observatory that is issued under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Intergovernmental Organization (CC BY-NC-SA 3.0 IGO) licence. WHO collects and provides access to the huge amount of data that is used by analysts every day. The GHO data repository is WHO's gateway to health-related statistics for its 194 Member States. It provides access to over 1000 indicators on priority health topics.

Photo by Anastasia Zhenina on Unsplash

Inspiration

This dataset is better used with the combination with the other datasets that can help in getting additional insights. While detailed beverage type information is not present in this dataset, total alcohol consumption might be useful in analyzing alcohol consumption differences between countries and get interesting insights combining this data with mental health or tourist behavior, for example.

BackgroundAlthough excessive alcohol-related mortality in the post-Soviet countries remains the major public health threat, determinants of this phenomenon are still poorly understood.AimsWe assess simultaneously individual- and area-level factors associated with an elevated risk of alcohol-related mortality among Lithuanian males aged 30–64.MethodsOur analysis is based on a census-linked dataset containing information on individual- and area-level characteristics and death events which occurred between March 1st, 2011 and December 31st, 2013. We limit the analysis to a few causes of death which are directly linked to excessive alcohol consumption: accidental poisonings by alcohol (X45) and liver cirrhosis (K70 and K74). Multilevel Poisson regression models with random intercepts are applied to estimate mortality rate ratios (MRR).ResultsThe selected individual-level characteristics are important predictors of alcohol-related mortality, whereas area-level variables show much less pronounced or insignificant effects. Compared to married men, never married (MRR = 1.9, CI:1.6–2.2), divorced (MRR = 2.6, CI:2.3–2.9), and widowed (MRR = 2.4, CI: 1.8–3.1) men are disadvantaged groups. Men who have the lowest level of educational attainment have the highest mortality risk (MRR = 1.7 CI:1.4–2.1). Being unemployed is associated with a five-fold risk of alcohol-related death (MRR = 5.1, CI: 4.4–5.9), even after adjusting for all other individual variables. Lithuanian males have an advantage over Russian (MRR = 1.3, CI:1.1–1.6) and Polish (MRR = 1.8, CI: 1.5–2.2) males. After adjusting for all individual characteristics, only two out of seven area-level variables—i.e., the share of ethnic minorities in the population and the election turnout—have statistically significant direct associations. These variables contribute to a higher risk of alcohol-related mortality at the individual level.ConclusionsThe huge and increasing socio-economic disparities in alcohol-related mortality indicate that recently implemented anti-alcohol measures in Lithuania should be reinforced by specific measures targeting the most disadvantaged population groups and geographical areas.

Potential years of life lost (PYLL) due to alcohol-related conditions, all ages, directly age-standardised per 100,000 population (standardised to the ESP).

Rationale Alcohol consumption is a contributing factor to hospital admissions and deaths from a diverse range of conditions. Alcohol misuse is estimated to cost the NHS about £3.5 billion per year and society as a whole £21 billion annually. The Government has said that everyone has a role to play in reducing the harmful use of alcohol - this indicator is one of the key contributions by the Government (and the Department of Health and Social Care) to promote measurable, evidence-based prevention activities at a local level, and supports the national ambitions to reduce harm set out in the Government's Alcohol Strategy. This ambition is part of the monitoring arrangements for the Responsibility Deal Alcohol Network. Alcohol-related deaths can be reduced through local interventions to reduce alcohol misuse and harm.

Potential years of life lost (PYLL) is a measure of the potential number of years lost when a person dies prematurely. The basic concept of PYLL is that deaths at younger ages are weighted more heavily than those at older ages. The advantage in doing this is that deaths at younger ages may be seen as less important if cause-specific death rates were just used on their own in highlighting the burden of disease and injury, since conditions such as cancer and heart disease usually occur at older ages and have relatively high mortality rates.

To enable comparisons between areas and over time, PYLL rates are age-standardised to represent the PYLL if each area had the same population structure as the 2013 European Standard Population (ESP). PYLL rates are presented as years of life lost per 100,000 population.

Definition of numerator The number of age-specific alcohol-related deaths multiplied by the national life expectancy for each age group and summed to give the total potential years of life lost due to alcohol-related conditions.

Definition of denominator ONS Mid-Year Population Estimates aggregated into quinary age bands.

Caveats There is the potential for the underlying cause of death to be incorrectly attributed on the death certificate and the cause of death misclassified. Alcohol-attributable fractions were not available for children. Conditions where low levels of alcohol consumption are protective (have a negative alcohol-attributable fraction) are not included in the calculation of the indicator.

The national life expectancies for England have been used for all sub-national geographies to illustrate the disparities in the burden caused by alcohol between local areas and the national average.

The confidence intervals do not take into account the uncertainty involved in the calculation of the AAFs – that is, the proportion of deaths that are caused by alcohol and the alcohol consumption prevalence that are included in the AAF formula are only an estimate and so include uncertainty. The confidence intervals published here are based only on the observed number of deaths and do not account for this uncertainty in the calculation of attributable fraction - as such the intervals may be too narrow.

This dataset presents information on alcohol-attributable mortality rates for Alberta, for selected causes of death, per 100,000 population, for the years 2002 to 2012.

This dataset is deprecated and will be removed by the end of the calendar year 2024. Updated on 8/18/2024 Drug and alcohol-related Intoxication death data is prepared using drug and alcohol intoxication data housed in a registry developed and maintained by the Vital Statistics Administration (VSA) of the Maryland Department of Health and Mental Hygiene (DHMH). The methodology for reporting on drug-related intoxication deaths in Maryland was developed by VSA with assistance from the DHMH Alcohol and Drug Abuse Administration, the Office of the Chief Medical Examiner (OCME) and the Maryland Poison Control Center. Assistance was also provided by authors of a 2008 Baltimore City Health Department report on intoxication deaths. Data in this table is by incident location, where the death occurred, rather than by county of residence.

Description:

This dataset explores the factors influencing life expectancy across various countries and years, aiming to uncover patterns and disparities in health outcomes based on geographic locations. By examining key features such as adult mortality, alcohol consumption, healthcare expenditures, and socioeconomic indicators, this dataset provides insights into the complex interplay of factors shaping life expectancy worldwide.

Feature Description:

Source:

World Health Organization (WHO), United Nations (UN), World Bank, etc.

The Mortality - Multiple Cause of Death data on CDC WONDER are county-level national mortality and population data spanning the years 1999-2009. Data are based on death certificates for U.S. residents. Each death certificate contains a single underlying cause of death, up to twenty additional multiple causes (Boolean set analysis), and demographic data. The number of deaths, crude death rates, age-adjusted death rates, standard errors and 95% confidence intervals for death rates can be obtained by place of residence (total U.S., region, state, and county), age group (including infants and single-year-of-age cohorts), race (4 groups), Hispanic ethnicity, gender, year of death, and cause-of-death (4-digit ICD-10 code or group of codes, injury intent and mechanism categories, or drug and alcohol related causes), year, month and week day of death, place of death and whether an autopsy was performed. The data are produced by the National Center for Health Statistics.

Annual data on number of alcohol-specific deaths by sex, age group and individual cause of death, UK constituent countries.

Description

This Dataset contains details of Alcohol, total per capita (15+) consumption in litres of pure alcohol (3 Year Average).

Alcohol has historically and continues to hold an important role in social engagement and bonding for many human being. Social drinking or moderate alcohol consumption for many is pleasurable.

However, alcohol consumption (especially in excess) is linked to a number of negative outcomes and issues like risk factor for diseases and health impacts, crime, road incidents, and, for some, alcohol dependence.

Acknowledgements

https://www.who.int/

Photo by Adam Wilson on Unsplash

Country: The country to which the data belongs. Year: The year in which the data was collected. Status: Whether the country is classified as "Developing" or "Developed". Life expectancy (men): The average life expectancy of men in that country for that year. Life expectancy (women): The average life expectancy of women in that country for that year. Adult Mortality (men): The mortality rate amongst adult men in that country for that year. Adult Mortality (women): The mortality rate amongst adult women in that country for that year. Infant deaths: The number of infant deaths in that country for that year. Alcohol: Per capita alcohol consumption (in litres of pure alcohol) in that country for that year. Percentage expenditure: Expenditure on health as a percentage of Gross Domestic Product per capita(%). Hepatitis B (men): Hepatitis B vaccination coverage in men (%). Hepatitis B (women): Hepatitis B vaccination coverage in women (%). Measles: Number of reported cases of measles in that country for that year. BMI: Average Body Mass Index of the country's population. Under-five deaths: Number of deaths under five years old. Polio: Polio (Pol3) immunization coverage among 1-year-olds (%). Total expenditure: General government expenditure on health as a percentage of total government expenditure (%). Diphtheria: Diphtheria tetanus toxoid and pertussis (DTP3) immunization coverage among 1-year-olds (%). HIV/AIDS: Deaths per 1 000 live births HIV/AIDS (0-4 years). GDP: Gross Domestic Product per capita (in USD). Population: Population of the country. thinness 1-19 years: Prevalence of thinness among children and adolescents for Age 10 to 19 (%). thinness 5-9 years: Prevalence of thinness among children for Age 5 to 9(%). Income composition of resources: Human Development Index in terms of income composition of resources (index ranging from 0 to 1). Schooling: Number of years of Schooling(years).

This data shows premature deaths (Age under 75) from Liver Disease, numbers and rates by gender, as 3-year moving-averages. Most liver disease is preventable and much is influenced by alcohol consumption and obesity prevalence, which are both amenable to public health interventions. Directly Age-Standardised Rates (DASR) are shown in the data (where numbers are sufficient) so that death rates can be directly compared between areas. The DASR calculation applies Age-specific rates to a Standard (European) population to cancel out possible effects on crude rates due to different age structures among populations, thus enabling direct comparisons of rates. A limitation on using mortalities as a proxy for prevalence of health conditions is that mortalities may give an incomplete view of health conditions in an area, as ill-health might not lead to premature death. Low numbers may result in zero values or missing data. Data source: Office for Health Improvement and Disparities (OHID), Public Health Outcomes Framework (PHOF) indicator 40601 (E06a). The data is updated annually.

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Unveiling the Health and Demographics Dataset: Illuminating Life Expectancy

Description: Step into the world of global health and demographics with our rich and comprehensive dataset. It's your passport to unraveling the secrets of life expectancy and understanding the pulse of population health. Dive into a treasure trove of valuable information for public health research and epidemiology, where each column tells a unique story about a nation's health journey.

Discover the Gems in Our Dataset:

• Country: Explore the global tapestry with data from diverse nations.
• Year: Unlock the passage of time and its impact on health trends.
• Status: Understand the development status, whether "Developed" or "Developing," that shapes the course of health.
• Life Expectancy: Peer into the crystal ball of population health, revealing how long people can expect to live.
• Adult Mortality: Gauge the probabilities of survival between ages 15 and 60 per 1,000 population.
• Infant Deaths: Delve into infant health with the number of infant deaths per 1,000 live births.
• Alcohol: Raise a glass to insights on average alcohol consumption in liters per capita.
• Percentage Expenditure: Unearth health expenditure as a percentage of a country's GDP.
• Hepatitis B: Measure immunization coverage for Hepatitis B.
• Measles: Examine the impact of this preventable disease with the number of reported cases per 1,000 population.
• BMI: Step onto the scales of national health with the average Body Mass Index.
• Under-Five Deaths: Shine a spotlight on child mortality with the number of deaths under age five per 1,000 live births.
• Polio: Inspect immunization coverage for Polio.
• Total Expenditure: Track the total health expenditure as a percentage of GDP.
• Diphtheria: Assess immunization coverage for Diphtheria.
• HIV/AIDS: Witness the prevalence of HIV/AIDS as a percentage of the population.
• GDP: Follow the financial pulse of a nation with Gross Domestic Product data.
• Population: Witness the ebb and flow of a nation's populace.
• Thinness 1-19 Years: Explore the prevalence of thinness among children and adolescents aged 1-19.
• Thinness 5-9 Years: Zoom in on thinness among children aged 5-9.
• Income Composition of Resources: Decode the composite index reflecting income distribution and resource access.
• Schooling: Measure the gift of knowledge with data on average years of schooling.

Predictive Targets: - The "Life Expectancy" column is your North Star, guiding the way to predictive insights. Harness the power of data to predict life expectancy using the mosaic of health and demographic indicators at your disposal.

Journey with the Data: 1. Predicting Life Expectancy: Embark on the quest to build regression models that forecast life expectancy for diverse countries and years based on this wealth of features. 2. Identifying Influential Factors: Uncover the gems within the dataset that influence life expectancy the most, providing valuable insights for public health interventions. 3. Health Policy Analysis: Assess the impact of health expenditure, immunization coverage, and disease prevalence on life expectancy and shape policies that safeguard population health.

This dataset is your window into the intricate world of global health. Join us on a journey of discovery as we explore the factors shaping life expectancy and navigate the waters of public health, epidemiology, and population health.

Effect of suicide rates on life expectancy dataset

Abstract
In 2015, approximately 55 million people died worldwide, of which 8 million committed suicide. In the USA, one of the main causes of death is the aforementioned suicide, therefore, this experiment is dealing with the question of how much suicide rates affects the statistics of average life expectancy.
The experiment takes two datasets, one with the number of suicides and life expectancy in the second one and combine data into one dataset. Subsequently, I try to find any patterns and correlations among the variables and perform statistical test using simple regression to confirm my assumptions.

Data

The experiment uses two datasets - WHO Suicide Statistics[1] and WHO Life Expectancy[2], which were firstly appropriately preprocessed. The final merged dataset to the experiment has 13 variables, where country and year are used as index: Country, Year, Suicides number, Life expectancy, Adult Mortality, which is probability of dying between 15 and 60 years per 1000 population, Infant deaths, which is number of Infant Deaths per 1000 population, Alcohol, which is alcohol, recorded per capita (15+) consumption, Under-five deaths, which is number of under-five deaths per 1000 population, HIV/AIDS, which is deaths per 1 000 live births HIV/AIDS, GDP, which is Gross Domestic Product per capita, Population, Income composition of resources, which is Human Development Index in terms of income composition of resources, and Schooling, which is number of years of schooling.

LICENSE

THE EXPERIMENT USES TWO DATASET - WHO SUICIDE STATISTICS AND WHO LIFE EXPECTANCY, WHICH WERE COLLEECTED FROM WHO AND UNITED NATIONS WEBSITE. THEREFORE, ALL DATASETS ARE UNDER THE LICENSE ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 3.0 IGO (https://creativecommons.org/licenses/by-nc-sa/3.0/igo/).

[1] https://www.kaggle.com/szamil/who-suicide-statistics

[2] https://www.kaggle.com/kumarajarshi/life-expectancy-who

Features:

Country: The name of the country. Year: The year of data recording. Life Expectancy: The average number of years a newborn, person at different age ranges, or the entire population is expected to live Adult Mortality: Probability of dying between 15 and 60 years per 1000 population. Infant Deaths: Number of infant deaths per 1000 live births. Alcohol: Alcohol consumption per capita (in liters of pure alcohol). Percentage Expenditure: Expenditure on health as a percentage of total government spending or GDP. Hepatitis B: Hepatitis B immunization coverage among 1-year-olds (percentage). Measles: Measles immunization coverage among 1-year-olds (percentage).

Labels: "Status" (Development Status: Developing or Developed)

The Detailed Mortality - Underlying Cause of Death data on CDC WONDER are county-level national mortality and population data spanning the years 1999-2009. Data are based on death certificates for U.S. residents. Each death certificate contains a single underlying cause of death, and demographic data. The number of deaths, crude death rates, age-adjusted death rates, standard errors and 95% confidence intervals for death rates can be obtained by place of residence (total U.S., region, state, and county), age group (including infants and single-year-of-age cohorts), race (4 groups), Hispanic ethnicity, sex, year of death, and cause-of-death (4-digit ICD-10 code or group of codes, injury intent and mechanism categories, or drug and alcohol related causes), year, month and week day of death, place of death and whether an autopsy was performed. The data are produced by the National Center for Health Statistics.

This is historical data. The update frequency has been set to "Static Data" and is here for historic value. Updated on 8/14/2024 Drug-Induced Death Rate - This indicator shows the drug-induced death rate per 100,000 population. Drug-induced deaths include all deaths for which illicit or prescription drugs are the underlying cause. In 2007, drug-induced deaths were more common than alcohol-induced or firearm-related deaths in the United States. Between 2012-2014, there were 2793 drug-induced deaths in Maryland. Link to Data Details