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

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.

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 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.

Analysis of ‘Alcohol Related Deaths in the UK 1994 To 2016’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/alcohol-related-deaths-in-the-uk-1994-to-2016e on 13 February 2022.

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

About this dataset

This dataset includes information on age-standardized and age-specific alcohol-related death rates in the UK, its constituent countries and regions of England, deaths registered from 1994 to 2016.

COMMERCIAL LICENSE

For subscribing to a commercial license for John Snow Labs Data Library which includes all datasets curated and maintained by John Snow Labs please visit https://www.johnsnowlabs.com/marketplace.

This dataset was created by John and contains around 0 samples along with Deaths, Region Geography Code, technical information and other features such as: - Year - Rate Per 100000 Persons - and more.

How to use this dataset

• Analyze Gender in relation to Region Of England
• Study the influence of Deaths on Region Geography Code
• More datasets

Acknowledgements

If you use this dataset in your research, please credit John

Start A New Notebook!

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

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

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, 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.

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.

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

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.

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

The research on life expectancy in countries takes the spotlight in the notebook's machine learning model. Substantial data analysis and predictive algorithms are used to uncover the reasons causing differences in longevity among countries. With the aid of strong statistical tools, valuable insights into the complex link between healthcare, socioeconomic factors, and life expectancy are sought |Description|Column| |:------:|:--------:| |Country under study|Country| |year|Year| |Status of the country's development|Status| |Population of country|Population| |Percentage of people finally one year old who were immunized against hepatitis B|Hepatitis B| |The number of reported measles cases per 1000 people|Measles| |Percentage of 1-year-olds immunized against polio|Polio| |Percentage of people finally one year old who were immunized against diphtheria|Diphtheria| |The number of deaths caused by AIDS of the last 4-year-olds who were born alive per 1000 people|HIV/AIDS| |The number of infant deaths per 1000 people|infant deaths| |he number of deaths of people under 5 years old per 1000 people|under-five deaths| |The ratio of government medical-health expenses to total government expenses in percentage|Total expenditure| |Gross domestic product|GDP| |The average body mass index of the entire population of the country|BMI| |Prevalence of thinness among people 19 years old in percentage|thinness 1-19 years| |Liters of alcohol consumption among people over 15 years old|Alcohol| |The number of years that people study|Schooling| |Country life expectancy|Life expectancy [target variable]|

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

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.

Analysis of ‘💊 Drug Induced Deaths’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/drug-induced-deathse on 13 February 2022.

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

About this dataset

This data was compiled using the CDC's WONDER database using these parameters:

• Group By: State, Year
• Measures: Deaths, Population, Crude Rate (95% Confidence Interval and Standard Error)
• Underlying Cause of Death: UCD - Drug/Alcohol Induced Causes - Drug Induced Causes
• Show Totals
• Show Zero Values
• Show Suppressed Values

Citation

Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death
1999-2015 on CDC WONDER Online Database, released December, 2016. Data are from the Multiple Cause of Death Files, 1999-2015, as
compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed
at http://wonder.cdc.gov/mcd-icd10.html on November 3, 2017.

Caveats

1. As of April 3, 2017, the underlying cause of death has been revised for 125 deaths in 2014. More information:
   http://wonder.cdc.gov/wonder/help/mcd.html#2014-Revision.
2. Circumstances in Georgia for the years 2008 and 2009 have resulted in unusually high death counts for the ICD-10 cause of
   death code R99, "Other ill-defined and unspecified causes of mortality." Caution should be used in interpreting these data.
   More information: http://wonder.cdc.gov/wonder/help/mcd.html#Georgia-Reporting-Anomalies.
3. Circumstances in New Jersey for the year 2009 have resulted in unusually high death counts for the ICD-10 cause of death code
   R99, "Other ill-defined and unspecified causes of mortality" and therefore unusually low death counts in other ICD-10 codes,
   most notably R95, "Sudden Infant Death Syndrome" and X40-X49, "Unintentional poisoning." Caution should be used in
   interpreting these data. More information: http://wonder.cdc.gov/wonder/help/mcd.html#New-Jersey-Reporting-Anomalies.
4. Circumstances in California resulted in unusually high death counts for the ICD-10 cause of death code R99, "Other
   ill-defined and unspecified causes of mortality" for deaths occurring in years 2000 and 2001. Caution should be used in
   interpreting these data. More information: http://wonder.cdc.gov/wonder/help/mcd.html#California-Reporting-Anomalies.
5. 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/mcd.html#Unreliable.
6. The method used to calculate 95% confidence intervals is documented here: More information:
   http://wonder.cdc.gov/wonder/help/mcd.html#Confidence-Intervals.
7. The method used to calculate standard errors is documented here: More information:
   http://wonder.cdc.gov/wonder/help/mcd.html#Standard-Errors.
8. 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. Population figures for 2011 are bridged-race estimates of the
   July 1 resident population, from the county-level postcensal Vintage 2011 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 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.
9. 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/mcd.html#Age Group.

Source: http://wonder.cdc.gov/mcd-icd10.html

This dataset was created by Health and contains around 900 samples along with Crude Rate Lower 95% Confidence Interval, Crude Rate Standard Error, technical information and other features such as: - Year - Crude Rate - and more.

How to use this dataset

• Analyze Deaths in relation to Crude Rate Upper 95% Confidence Interval
• Study the influence of State on Crude Rate Lower 95% Confidence Interval
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Health

Start A New Notebook!

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

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.

Data underlying figures and relative risk curves within the article. Provides readers the mean value and uncertainty intervals for prevalence of current drinking, drinks per day by location, relative risks by outcome and dose, along with results for the weighted all-cause relative risk curve used to justify TMREL within the study. Based off sources mentioned in Appendix I.

From Abstract in linked paper:

Background Alcohol use is a leading risk factor for death and disability, but its overall association with health remains complex given the possible protective effects of moderate alcohol consumption on some conditions. With our comprehensive approach to health accounting within the Global Burden of Diseases, Injuries, and Risk Factors Study 2016, we generated improved estimates of alcohol use and alcohol-attributable deaths and disability-adjusted life-years (DALYs) for 195 locations from 1990 to 2016, for both sexes and for 5-year age groups between the ages of 15 years and 95 years and older.

Methods Using 694 data sources of individual and population-level alcohol consumption, along with 592 prospective and retrospective studies on the risk of alcohol use, we produced estimates of the prevalence of current drinking, abstention, the distribution of alcohol consumption among current drinkers in standard drinks daily (defined as 10 g of pure ethyl alcohol), and alcohol-attributable deaths and DALYs. We made several methodological improvements compared with previous estimates: first, we adjusted alcohol sales estimates to take into account tourist and unrecorded consumption; second, we did a new meta-analysis of relative risks for 23 health outcomes associated with alcohol use; and third, we developed a new method to quantify the level of alcohol consumption that minimises the overall risk to individual health

BackgroundSocioeconomic inequalities in alcohol-related mortality have been documented in several European countries, but it is unknown whether the magnitude of these inequalities differs between countries and whether these inequalities increase or decrease over time.Methods and FindingsWe collected and harmonized data on mortality from four alcohol-related causes (alcoholic psychosis, dependence, and abuse; alcoholic cardiomyopathy; alcoholic liver cirrhosis; and accidental poisoning by alcohol) by age, sex, education level, and occupational class in 20 European populations from 17 different countries, both for a recent period and for previous points in time, using data from mortality registers. Mortality was age-standardized using the European Standard Population, and measures for both relative and absolute inequality between low and high socioeconomic groups (as measured by educational level and occupational class) were calculated.Rates of alcohol-related mortality are higher in lower educational and occupational groups in all countries. Both relative and absolute inequalities are largest in Eastern Europe, and Finland and Denmark also have very large absolute inequalities in alcohol-related mortality. For example, for educational inequality among Finnish men, the relative index of inequality is 3.6 (95% CI 3.3–4.0) and the slope index of inequality is 112.5 (95% CI 106.2–118.8) deaths per 100,000 person-years. Over time, the relative inequality in alcohol-related mortality has increased in many countries, but the main change is a strong rise of absolute inequality in several countries in Eastern Europe (Hungary, Lithuania, Estonia) and Northern Europe (Finland, Denmark) because of a rapid rise in alcohol-related mortality in lower socioeconomic groups. In some of these countries, alcohol-related causes now account for 10% or more of the socioeconomic inequality in total mortality.Because our study relies on routinely collected underlying causes of death, it is likely that our results underestimate the true extent of the problem.ConclusionsAlcohol-related conditions play an important role in generating inequalities in total mortality in many European countries. Countering increases in alcohol-related mortality in lower socioeconomic groups is essential for reducing inequalities in mortality. Studies of why such increases have not occurred in countries like France, Switzerland, Spain, and Italy can help in developing evidence-based policies in other European countries.