Deaths from alcohol-related conditions, all ages, directly age-standardised rate per 100,000 population (standardised to the European standard population).

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.

The proportion of disease attributable to alcohol (alcohol attributable fraction) is calculated using a relative risk (a fraction between 0 and 1) specific to each disease, age group, and sex combined with the prevalence of alcohol consumption in the population. All mortality records are extracted that contain an attributable disease and the age and sex-specific fraction applied. The results are summed into quinary age bands for the numerator and a directly standardised rate calculated using the European Standard Population. This revised indicator uses updated alcohol attributable fractions, based on new relative risks from ‘Alcohol-attributable fractions for England: an update’ (1) published by PHE in 2020. A detailed comparison between the 2013 and 2020 alcohol attributable fractions is available in Appendix 3 of the PHE report (2). A consultation was also undertaken with stakeholders where the impact of the new methodology on the LAPE indicators was quantified and explored (3).

The calculation that underlies all alcohol-related indicators has been updated to take account of the latest academic evidence and more recent alcohol-consumption figures. The result has been that the newly published mortality and admission rates are lower than those previously published. This is due to a change in methodology, mainly because alcohol consumption across the population has reduced since 2010. Therefore, the number of deaths and hospital admissions that we attribute to alcohol has reduced because in general people are drinking less today than they were when the original calculation was made.

Figures published previously did not misrepresent the burden of alcohol based on the previous evidence – the methodology used in this update is as close as sources and data allow to the original method. Though the number of deaths and admissions attributed to alcohol each year has reduced, the direction of trend and the key inequalities due to alcohol harm remain the same. Alcohol remains a significant burden on the health of the population and the harm alcohol causes to individuals remains unchanged.

References:

PHE (2020) Alcohol-attributable fractions for England: an update PHE (2020) Alcohol-attributable fractions for England: an update: Appendix 3 PHE (2021) Proposed changes for calculating alcohol-related mortality

Definition of numerator Deaths from alcohol-related conditions based on underlying cause of death, registered in the calendar year for all ages. Each alcohol-related death is assigned an alcohol attributable fraction based on underlying cause of death (and all cause of deaths fields for the conditions: ethanol poisoning, methanol poisoning, toxic effect of alcohol). Alcohol-attributable fractions were not available for children.

Mortality data includes all deaths registered in the calendar year where the local authority of usual residence of the deceased is one of the English geographies and an alcohol attributable diagnosis is given as the underlying cause of death. Counts of deaths for years up to and including 2019 have been adjusted where needed to take account of the MUSE ICD-10 coding change introduced in 2020. Detailed guidance on the MUSE implementation is available at: MUSE implementation guidance.

Counts of deaths for years up to and including 2013 have been double adjusted by applying comparability ratios from both the IRIS coding change and the MUSE coding change where needed to take account of both the MUSE ICD-10 coding change and the IRIS ICD-10 coding change introduced in 2014. The detailed guidance on the IRIS implementation is available at: IRIS implementation guidance.

Counts of deaths for years up to and including 2010 have been triple adjusted by applying comparability ratios from the 2011 coding change, the IRIS coding change, and the MUSE coding change where needed to take account of the MUSE ICD-10 coding change, the IRIS ICD-10 coding change, and the ICD-10 coding change introduced in 2011. The detailed guidance on the 2011 implementation is available at: 2011 implementation guidance.

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 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.

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

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

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.

Attitude to use of alcohol and judgement on the dangers of alcohol. Topics: intentions for the future; smoking; type of preferred use of tobacco; frequency of ´rolling your own´; frequency of use of selected alcoholic beverages; occasions for use of alcohol; preferred alcoholic beverages; reaction to an exclusively non-alcoholic selection of beverages at a party; assessment of personal use of alcohol in relation to others; estimated frequency of drinking alcohol among Germans; age limit for starting use of alcohol and attitude to use of alcohol among young people; accepted reasons for alcohol excesses; detailed information on type and amount of alcoholic drinks consumed in the last week; existence of friends who have strongly restricted use of alcohol; primary motives for use of alcohol; expected development of the use of alcohol given hypothetical increase in price; general evaluation of use of alcohol in the FRG; knowledge and characterization of someone suffering from alcoholism; attitude to drinking with anxiety; population groups particularly susceptible to alcoholism; perception of warnings before abuse of alcohol; personal unemployment in the last two years; who cooks in the household; membership in the trade union; size of place of residence and characterization of place of residence. Demography: age; sex; marital status; religious denomination; frequency of church attendance; school education; occupational position; employment; income; household size; household composition; respondent is head of household; residential status; city size; degree of urbanization; union membership. Interviewer rating: city size; social class of respondent. Einstellung zum Alkoholkonsum und Beurteilung der Gefahren des Alkohols. Themen: Vorsätze für die Zukunft; Rauchkonsum; Art des präferierten Tabakkonsums; Häufigkeit des "Selbstdrehens"; Häufigkeit des Genusses ausgewählter alkoholischer Getränke; Anlässe für Alkoholkonsum; präferiertes Alkoholgetränk; Reaktion auf ein ausschließlich alkoholfreies Getränkeangebot bei einem Fest; Einschätzung des eigenen Alkoholkonsums in Relation zu anderen; geschätzte Trinkhäufigkeit von Alkohol bei den Deutschen; Altersgrenze für den Beginn von Alkoholkonsum und Einstellung zum Alkoholkonsum bei Jugendlichen; akzeptierte Gründe für Alkoholexzesse; detaillierte Angaben über Art und Menge konsumierter Alkoholika in der vergangenen Woche; Existenz von Bekannten, die den Alkoholkonsum stark eingeschränkt haben; Hauptmotive für Alkoholkonsum; vermutete Entwicklung des Alkoholkonsums bei angenommener Verteuerung; allgemeine Bewertung des Alkoholkonsums in der BRD; Kenntnis und Charakterisierung eines Alkoholsüchtigen; Einstellung zum Trinken bei Sorgen; Bevölkerungsgruppen, die besonders anfällig für Alkoholismus sind; Wahrnehmung von Warnungen vor Alkoholmißbrauch; eigene Arbeitslosigkeit in den letzten zwei Jahren; wer kocht im Haushalt; Mitgliedschaft in der Gewerkschaft; Wohnortgröße und Charakterisierung des Wohnorts. Demographie: Alter; Geschlecht; Familienstand; Konfession; Kirchgangshäufigkeit; Schulbildung; Berufliche Position; Berufstätigkeit; Einkommen; Haushaltsgröße; Haushaltszusammensetzung; Befragter ist Haushaltsvorstand; Wohnstatus; Ortsgröße; Urbanisierungsgrad; Gewerkschaftsmitgliedschaft. Interviewerrating: Ortsgröße; Schichtzugehörigkeit des Befragten.

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).

This data set depicts unintentional overdose deaths by county for Tennessee from 1999-2017.Data was compiled from the CDC Wonder database for each year and combined into a single spreadsheet. Each year has both a death field and a rate of fatalities per 100,000 people. The CDC does not publish the number of fatalities by county if the total is less than 10 in a given year. The CDC does not post a rate of fatalities if the total number of deaths per county is less than 20. The population field contains estimates from 2018 and is NOT the data used to generate the rates over time.The following details are copied directly from the CDC Wonder database text file. Note that the year is different for each data download from the original database."Dataset: Underlying Cause of Death, 1999-2017""Query Parameters:""Drug/Alcohol Induced Causes: Drug poisonings (overdose) Unintentional (X40-X44)""States: Tennessee (47)""Year/Month: 1999""Group By: County""Show Totals: True""Show Zero Values: False""Show Suppressed: False""Calculate Rates Per: 100,000""Rate Options: Default intercensal populations for years 2001-2009 (except Infant Age Groups)""---""Help: See http://wonder.cdc.gov/wonder/help/ucd.html for more information.""---""Query Date: Aug 19, 2019 10:22:15 PM""1. Rows with suppressed Deaths are hidden, but the Deaths and Population values in those rows are included in the totals. Use""Quick Options above to show suppressed rows.""---"Caveats:"1. Data are Suppressed when the data meet the criteria for confidentiality constraints. More information:""http://wonder.cdc.gov/wonder/help/ucd.html#Assurance of Confidentiality.""2. Death rates are flagged as Unreliable when the rate is calculated with a numerator of 20 or less. More information:""http://wonder.cdc.gov/wonder/help/ucd.html#Unreliable.""3. The population figures for year 2017 are bridged-race estimates of the July 1 resident population, from the Vintage 2017""postcensal series released by NCHS on June 27, 2018. The population figures for year 2016 are bridged-race estimates of the July""1 resident population, from the Vintage 2016 postcensal series released by NCHS on June 26, 2017. The population figures for""year 2015 are bridged-race estimates of the July 1 resident population, from the Vintage 2015 postcensal series released by NCHS""on June 28, 2016. The population figures for year 2014 are bridged-race estimates of the July 1 resident population, from the""Vintage 2014 postcensal series released by NCHS on June 30, 2015. The population figures for year 2013 are bridged-race""estimates of the July 1 resident population, from the Vintage 2013 postcensal series released by NCHS on June 26, 2014. The""population figures for year 2012 are bridged-race estimates of the July 1 resident population, from the Vintage 2012 postcensal""series released by NCHS on June 13, 2013. The population figures for year 2011 are bridged-race estimates of the July 1 resident""population, from the Vintage 2011 postcensal series released by NCHS on July 18, 2012. Population figures for 2010 are April 1""Census counts. The population figures for years 2001 - 2009 are bridged-race estimates of the July 1 resident population, from""the revised intercensal county-level 2000 - 2009 series released by NCHS on October 26, 2012. Population figures for 2000 are""April 1 Census counts. Population figures for 1999 are from the 1990-1999 intercensal series of July 1 estimates. Population""figures for the infant age groups are the number of live births.
Note: Rates and population figures for years 2001 -""2009 differ slightly from previously published reports, due to use of the population estimates which were available at the time""of release.""4. The population figures used in the calculation of death rates for the age group 'under 1 year' are the estimates of the""resident population that is under one year of age. More information: http://wonder.cdc.gov/wonder/help/ucd.html#Age Group."

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.

Context

In the shadows of the Covid-19 pandemic, there is another global health crisis that has gone largely unnoticed. This is the Noncommunicable Disease (NCD) pandemic.

The WHO website describes NCDs as follows:

Noncommunicable diseases (NCDs), also known as chronic diseases, tend to be of long duration and are the result of a combination of genetic, physiological, environmental and behaviours factors.

The main types of NCDs are cardiovascular diseases (like heart attacks and stroke), cancers, chronic respiratory diseases (such as chronic obstructive pulmonary disease and asthma) and diabetes.

NCDs disproportionately affect people in low- and middle-income countries where more than three quarters of global NCD deaths – 32million – occur.

Key facts:

• Noncommunicable diseases (NCDs) kill 41 million people each year, equivalent to 71% of all deaths globally.
• Each year, 15 million people die from a NCD between the ages of 30 and 69 years; over 85% of these "premature" deaths occur in low- and middle-income > * countries.
• Cardiovascular diseases account for most NCD deaths, or 17.9 million people annually, followed by cancers (9.0 million), respiratory diseases (3.9million), and diabetes (1.6 million).
• These 4 groups of diseases account for over 80% of all premature NCD deaths.
• Tobacco use, physical inactivity, the harmful use of alcohol and unhealthy diets all increase the risk of dying from a NCD.
• Detection, screening and treatment of NCDs, as well as palliative care, are key components of the response to NCDs.

Content

This data repository consists of 3 CSV files: WHO-cause-of-death-by-NCD.csv is the main dataset, which provides the percentage of deaths caused by NCDs out of all causes of death, for each nation globally. Metadata_Country.csv and Metadata_Indicator.csv provide additional metadata which is helpful for interpreting the main CSV.

The data collected spans a period from 2000 to 2016. The main CSV has columns for every year from 1960 to 2019. It is advisable to drop all redundant columns where no data was collected.

Furthermore, it is advisable to merge Metadata_Country.csv with the main CSV as it provides valuable additional information, particularly on the economic situation of each nation.

Acknowledgements

This dataset has been extracted from The World Bank 'Cause of death, by non-communicable diseases (% of total)' Dataset, derived based on the data from WHO's Global Health Estimates. It is freely provided under a Creative Commons Attribution 4.0 International License (CC BY 4.0), with the additional terms as stated on the World Bank website: World Bank Terms of Use for Datasets.

Inspiration

I would be interested to see some good data wrangling (dropping redundant columns), as well as kernels interpreting additional information in 'SpecialNotes' column in Metadata_country.csv

It would also be great to see what different factors influence NCDs: most of all, the geopolitical factors. Would be great to see some choropleth visualisations to get an idea of which regions are most affected by NCDs.

Annual number of deaths registered related to drug poisoning in England and Wales by sex, region and whether selected substances were mentioned anywhere on the death certificate, with or without other drugs or alcohol, and involvement in suicides.

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.

Einstellung zum Alkoholkonsum und Beurteilung der Gefahren desAlkohols. Themen: Vorsätze für die Zukunft; Rauchkonsum; Art des präferiertenTabakkonsums; Häufigkeit des "Selbstdrehens"; Häufigkeit des Genussesausgewählter alkoholischer Getränke; Anlässe für Alkoholkonsum;präferiertes Alkoholgetränk; Reaktion auf ein ausschließlichalkoholfreies Getränkeangebot bei einem Fest; Einschätzung des eigenenAlkoholkonsums in Relation zu anderen; geschätzte Trinkhäufigkeit vonAlkohol bei den Deutschen; Altersgrenze für den Beginn von Alkoholkonsumund Einstellung zum Alkoholkonsum bei Jugendlichen; akzeptierte Gründefür Alkoholexzesse; detaillierte Angaben über Art und Menge konsumierterAlkoholika in der vergangenen Woche; Existenz von Bekannten, die denAlkoholkonsum stark eingeschränkt haben; Hauptmotive für Alkoholkonsum;vermutete Entwicklung des Alkoholkonsums bei angenommener Verteuerung;allgemeine Bewertung des Alkoholkonsums in der BRD; Kenntnis undCharakterisierung eines Alkoholsüchtigen; Einstellung zum Trinken beiSorgen; Bevölkerungsgruppen, die besonders anfällig für Alkoholismussind; Wahrnehmung von Warnungen vor Alkoholmißbrauch; eigeneArbeitslosigkeit in den letzten zwei Jahren; wer kocht im Haushalt;Mitgliedschaft in der Gewerkschaft; Wohnortgröße und Charakterisierungdes Wohnorts. Demographie: Alter; Geschlecht; Familienstand; Konfession;Kirchgangshäufigkeit; Schulbildung; Berufliche Position;Berufstätigkeit; Einkommen; Haushaltsgröße; Haushaltszusammensetzung;Befragter ist Haushaltsvorstand; Wohnstatus; Ortsgröße;Urbanisierungsgrad; Gewerkschaftsmitgliedschaft. Interviewerrating: Ortsgröße; Schichtzugehörigkeit des Befragten. Attitude to use of alcohol and judgement on the dangers of alcohol. Topics: intentions for the future; smoking; type of preferred use oftobacco; frequency of ´rolling your own´; frequency of use of selectedalcoholic beverages; occasions for use of alcohol; preferred alcoholicbeverages; reaction to an exclusively non-alcoholic selection ofbeverages at a party; assessment of personal use of alcohol in relationto others; estimated frequency of drinking alcohol among Germans; agelimit for starting use of alcohol and attitude to use of alcohol amongyoung people; accepted reasons for alcohol excesses; detailedinformation on type and amount of alcoholic drinks consumed in the lastweek; existence of friends who have strongly restricted use of alcohol;primary motives for use of alcohol; expected development of the use ofalcohol given hypothetical increase in price; general evaluation of useof alcohol in the FRG; knowledge and characterization of someonesuffering from alcoholism; attitude to drinking with anxiety;population groups particularly susceptible to alcoholism; perception ofwarnings before abuse of alcohol; personal unemployment in the last twoyears; who cooks in the household; membership in the trade union; sizeof place of residence and characterization of place of residence. Demography: age; sex; marital status; religious denomination;frequency of church attendance; school education; occupationalposition; employment; income; household size; household composition;respondent is head of household; residential status; city size; degreeof urbanization; union membership. Interviewer rating: city size; social class of respondent.

Data was compiled from the CDC Wonder database for each year and combined into a single spreadsheet. Each year has both a death field and a rate of fatalities per 100,000 people. The CDC does not publish the number of fatalities by county if the total is less than 10 in a given year. The CDC does not post a rate of fatalities if the total number of deaths per county is less than 20. The population field contains estimates from 2018 and is NOT the data used to generate the rates over time.The following details are copied directly from the CDC Wonder database text file. Note that the year is different for each data download from the original database."Dataset: Underlying Cause of Death, 1999-2017""Query Parameters:""Drug/Alcohol Induced Causes: Drug poisonings (overdose) Unintentional (X40-X44)""States: Tennessee (47)""Year/Month: 1999""Group By: County""Show Totals: True""Show Zero Values: False""Show Suppressed: False""Calculate Rates Per: 100,000""Rate Options: Default intercensal populations for years 2001-2009 (except Infant Age Groups)""---""Help: See http://wonder.cdc.gov/wonder/help/ucd.html for more information.""---""Query Date: Aug 19, 2019 10:22:15 PM""1. Rows with suppressed Deaths are hidden, but the Deaths and Population values in those rows are included in the totals. Use""Quick Options above to show suppressed rows.""---"Caveats:"1. Data are Suppressed when the data meet the criteria for confidentiality constraints. More information:""http://wonder.cdc.gov/wonder/help/ucd.html#Assurance of Confidentiality.""2. Death rates are flagged as Unreliable when the rate is calculated with a numerator of 20 or less. More information:""http://wonder.cdc.gov/wonder/help/ucd.html#Unreliable.""3. The population figures for year 2017 are bridged-race estimates of the July 1 resident population, from the Vintage 2017""postcensal series released by NCHS on June 27, 2018. The population figures for year 2016 are bridged-race estimates of the July""1 resident population, from the Vintage 2016 postcensal series released by NCHS on June 26, 2017. The population figures for""year 2015 are bridged-race estimates of the July 1 resident population, from the Vintage 2015 postcensal series released by NCHS""on June 28, 2016. The population figures for year 2014 are bridged-race estimates of the July 1 resident population, from the""Vintage 2014 postcensal series released by NCHS on June 30, 2015. The population figures for year 2013 are bridged-race""estimates of the July 1 resident population, from the Vintage 2013 postcensal series released by NCHS on June 26, 2014. The""population figures for year 2012 are bridged-race estimates of the July 1 resident population, from the Vintage 2012 postcensal""series released by NCHS on June 13, 2013. The population figures for year 2011 are bridged-race estimates of the July 1 resident""population, from the Vintage 2011 postcensal series released by NCHS on July 18, 2012. Population figures for 2010 are April 1""Census counts. The population figures for years 2001 - 2009 are bridged-race estimates of the July 1 resident population, from""the revised intercensal county-level 2000 - 2009 series released by NCHS on October 26, 2012. Population figures for 2000 are""April 1 Census counts. Population figures for 1999 are from the 1990-1999 intercensal series of July 1 estimates. Population""figures for the infant age groups are the number of live births.
Note: Rates and population figures for years 2001 -""2009 differ slightly from previously published reports, due to use of the population estimates which were available at the time""of release.""4. The population figures used in the calculation of death rates for the age group 'under 1 year' are the estimates of the""resident population that is under one year of age. More information: http://wonder.cdc.gov/wonder/help/ucd.html#Age Group."

Admissions to hospital for under 18s where the primary diagnosis or any of the secondary diagnoses are an alcohol-specific (wholly attributable) condition. Crude rate per 100,000 population.

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) 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 admissions can be reduced through local interventions to reduce alcohol misuse and harm.

Reducing alcohol-related harm is one of Public Health England’s seven priorities for the next five years (from the “Evidence into action” report 2014).

The Sexual Health Framework (2013) highlights the following:

There is an association between alcohol-attributable hospital admissions in both males and females with teenage pregnancy, even after controlling for the overriding and strong effect of deprivation, and the same is true for the more common sexually transmitted infections. There is evidence that alcohol consumption and being drunk can result in lower inhibitions and poor judgements regarding sexual activity, vulnerability, and risky sexual behaviour, such as not using contraception or condoms. Alcohol consumption by young people leads to an increased likelihood that they will have sex at a younger age, and alcohol misuse is linked to a greater number of sexual partners and more regretted or coerced sex. Alcohol also increases the risk of sexual aggression, sexual violence, and sexual victimisation of women.

Definition of numerator The number of hospital admission episodes for under 18s where the primary diagnosis or any of the secondary diagnoses are an alcohol-specific (wholly attributable) condition code only.

More specifically, hospital admissions records are identified where:

The admission is a finished episode [epistat = 3]; The admission is an ordinary admission, day case, or maternity [classpat = 1, 2, or 5]; It is an admission episode [epiorder = 1]; The sex of the patient is valid [sex = 1 or 2]; There is a valid age at start of episode [startage between 0 and 150 or between 7001 and 7007]; The region of residence is one of the English regions, no fixed abode, or unknown [resgor <= K or U or Y]; The episode end date [epiend] falls within the financial year; A wholly alcohol-attributable ICD10 code appears in any diagnosis field [diag_nn].

Definition of denominator ONS mid-year population estimates for 0-17 year olds. Three years are pooled.

Caveats In 2023, NHS England announced a requirement for Trusts to report Same Day Emergency Care (SDEC) to the Emergency Care Data Set (ECDS) by July 2024. Early adopter sites began to report SDEC to ECDS from 2021/22, with other Trusts changing their reporting in 2022/23 or 2023/24. Some Trusts had previously reported this activity as part of the Admitted Patient Care data set, and moving to report to ECDS may reduce the number of admissions reported for this/these indicator/s. NHSE have advised it is not possible accurately to identify SDEC in current data flows, but the impact of the change is expected to vary by diagnosis, with indicators related to injuries and external causes potentially most affected.

When considering if SDEC recording practice has reduced the number of admissions reported for this indicator at local level, please refer to the list of sites who have reported when they began to report SDEC to ECDS.

Hospital admission data can be coded differently in different parts of the country. In some cases, details of the patient's residence are insufficient to allocate the patient to a particular area and in other cases, the patient has no fixed abode. These cases are included in the England total but not in the local authority or PHE centre figures. Conditions where low levels of alcohol consumption are protective (have a negative alcohol-attributable fraction) are not included in the calculation of the indicator. Does not include attendance at Accident and Emergency departments.

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).

Abstract copyright UK Data Service and data collection copyright owner.The Smoking, Drinking and Drug Use among Young People surveys began in 1982, under the name Smoking among Secondary Schoolchildren. The series initially aimed to provide national estimates of the proportion of secondary schoolchildren aged 11-15 who smoked, and to describe their smoking behaviour. Similar surveys were carried out every two years until 1998 to monitor trends in the prevalence of cigarette smoking. The survey then moved to an annual cycle, and questions on alcohol consumption and drug use were included. The name of the series changed to Smoking, Drinking and Drug Use among Young Teenagers to reflect this widened focus. In 2000, the series title changed, to Smoking, Drinking and Drug Use among Young People. NHS Digital (formerly the Information Centre for Health and Social Care) took over from the Department of Health as sponsors and publishers of the survey series from 2005. From 2014 onwards, the series changed to a biennial one, with no survey taking place in 2015, 2017 or 2019.In some years, the surveys have been carried out in Scotland and Wales as well as England, to provide separate national estimates for these countries. In 2002, following a review of Scotland's future information needs in relation to drug misuse among schoolchildren, a separate Scottish series, Scottish Schools Adolescent Lifestyle and Substance Use Survey (SALSUS) was established by the Scottish Executive. The survey uses a two-stage probability sample of schools and pupils, designed to be representative of young people aged between 11 and 15. The sample of schools is stratified by sex of intake and school type. Within these strata, the sampling frame is sorted by local authority. This design does not guarantee a representative sample of schools within all regions and so reliable estimates by region cannot currently be derived from any one year’s data. This dataset contains regional information as well as key survey variables from the three most recent survey years, 2006 to 2008, combined and weighted to be regionally representative. Main Topics: The dataset includes core responses from all pupils who completed a questionnaire in survey years 2006 to 2008. Broad topics included: smokingdrinkingdrug useattitudes to smoking, drinking and drug useeducationtruancy and exclusionbackground information Multi-stage stratified random sample Self-completion

BackgroundAlcohol has been linked to health disparities between races in the US; however, race-specific alcohol-attributable mortality has never been estimated. The objective of this article is to estimate premature mortality attributable to alcohol in the US in 2005, differentiated by race, age and sex for people 15 to 64 years of age. Methods and FindingsMortality attributable to alcohol was estimated based on alcohol-attributable fractions using indicators of exposure from the National Epidemiologic Survey on Alcohol and Related Conditions and risk relations from the Comparative Risk Assessment study. Consumption data were corrected for undercoverage (the observed underreporting of alcohol consumption when using survey as compared to sales data) using adult per capita consumption from WHO databases. Mortality data by cause of death were obtained from the US Department of Health and Human Services. For people 15 to 64 years of age in the US in 2005, alcohol was responsible for 55,974 deaths (46,461 for men; 9,513 for women) representing 9.0% of all deaths, and 1,288,700 PYLL (1,087,280 for men; 201,420 for women) representing 10.7% of all PYLL. Per 100,000 people, this represents 29 deaths (29 for White; 40 for Black; 82 for Native Americans; 6 for Asian/Pacific Islander) and 670 PYLL (673 for White; 808 for Black; 1,808 for Native American; 158 for Asian/Pacific Islander). Sensitivity analyses showed a lower but still substantial burden without adjusting for undercoverage. ConclusionsThe burden of mortality attributable to alcohol in the US is unequal among people of different races and between men and women. Racial differences in alcohol consumption and the resulting harms explain in part the observed disparities in the premature mortality burden between races, suggesting the need for interventions for specific subgroups of the population such as Native Americans.

This dataset contains frequencies, rates, and proportions that describe drug toxicity deaths in Nova Scotia over time and space and by certain demographic and contextual characteristics. See usage considerations for further details on these data.

Cambodia KH: Total Alcohol Consumption per Capita: Liters of Pure Alcohol: Projected Estimates: Aged 15+ data was reported at 6.670 l/Person in 2020. This records a decrease from the previous number of 8.480 l/Person for 2019. Cambodia KH: Total Alcohol Consumption per Capita: Liters of Pure Alcohol: Projected Estimates: Aged 15+ data is updated yearly, averaging 4.710 l/Person from Dec 2000 (Median) to 2020, with 21 observations. The data reached an all-time high of 8.480 l/Person in 2019 and a record low of 1.810 l/Person in 2001. Cambodia KH: Total Alcohol Consumption per Capita: Liters of Pure Alcohol: Projected Estimates: Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Cambodia – Table KH.World Bank.WDI: Social: Health Statistics. Total alcohol per capita consumption is defined as the total (sum of recorded and unrecorded alcohol) amount of alcohol consumed per person (15 years of age or older) over a calendar year, in litres of pure alcohol, adjusted for tourist consumption.;World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).;Weighted average;This is the Sustainable Development Goal indicator 3.5.2[https://unstats.un.org/sdgs/metadata/].