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

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.

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.

This dataset contains all the data tables related to the annual data on number of alcohol-specific deaths by sex, age group and individual cause of death, UK constituent countries. Published in March 2023 by the Office for National Statistics (ONS) from 2001 - 2021

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.

Alcohol Induced Death reports the number and rate of alcohol-induced deaths. Dimensions Year,Measure.Type,Variable Full Description Deaths from accidents, homicides, and other causes indirectly related to alcohol use, as well as newborn deaths associated with maternal alcohol use are excluded from this count. The age-adjusted mortality rate (AAMR) controls for the impact of different age structures in order to better evaluate risk levels that are independent of the age composition of the population. Connecticut Department of Public Health collects data annually. CTdata.org carries three year aggregations of annual data.

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.

Alcohol-Impaired Driving Fatalities 2005-2014; All persons killed in crashes involving a driver with BAC >= .08 g/dL. Occupant Fatalities 2005-2014; All occupants killed where body type = 1-79. Source: National Highway Traffic Safety Administration's (NHTSA) Fatality Analysis Reporting System (FARS), 2005-2013 Final Reports and 2014 Annual Report File

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.

Admissions to hospital where the primary diagnosis or any of the secondary diagnoses are an alcohol-specific (wholly attributable) condition. 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) 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).

Definition of numerator Admissions to hospital 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.

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 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.

In order to allow comparison of groups with different age structures it is common to present “age standardised” rates. These are calculated by summing the product of age specific rates for each age band in the group by the number in that age band in the standard population. The sum is then divided by the total number in all age bands in the standard population to obtain the age standardised rate. This improves the comparability of rates for different areas, or between different time periods, by taking into account differences in the age structure of the populations being compared. Any difference between groups in age standardised rates is then not due to difference in age structure since the same standard population was used to calculate all age standardised rates. The method does however assume that minor differences in age structure within age bands are unimportant and in general this is true.

The number of Motor Vehicle fatalities in British Columbia with Alcohol and/or Drug involvement by year. A motor vehicle fatality is a road user (driver, passenger, pedestrian, and cyclist) who is injured in a collision involving a motor vehicle on a 'public highway' as defined in the Motor Vehicle Act and the victim is deceased within 30 days of the collision as a result of their injuries. This data excludes fatal victims on roads where the Motor Vehicle Act does not apply (such as forest-service roads, industrial roads and private driveways); fatal victims of off-road snow mobile accidents; and homicides or suicides.

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.

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

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

This topic page looks at the data on global patterns of alcohol consumption, patterns of drinking, beverage types, the prevalence of alcoholism, and consequences, including crime, mortality, and road incidents.

Related topics:

Data on other drug use can be found on our full topic page here.

Drug use disorders are often classified within the same category as mental health disorders — research and data on mental health can be found on our topic page here.

Support for alcohol dependency

At the end of this topic page, you will find additional resources and guidance if you, or someone you know, needs support in dealing with alcohol dependency.

Alcohol consumption across the world today This interactive map shows the annual average alcohol consumption of alcohol, expressed per person aged 15 years or older. To account for the differences in alcohol content of different alcoholic drinks (e.g., beer, wine, spirits), this is reported in liters of pure alcohol per year.

To make this average more understandable, we can express it in bottles of wine. Wine contains around 12% pure alcohol per volume1 so that one liter of wine contains 0.12 liters of pure alcohol. So, a value of 6 liters of pure alcohol per person per year is equivalent to 50 bottles of wine per year.

As the map shows, the average per capita alcohol consumption varies widely globally.

We see large geographical differences: Alcohol consumption across North Africa and the Middle East is particularly low — in many countries, close to zero. At the upper end of the scale, alcohol intake across Europe is higher.

Share of adults who drink alcohol This interactive map shows the share of adults who drink alcohol. This is given as the share of adults aged 15 years and older who have drunk alcohol within the previous year.

In many countries, the majority of adults drink some alcohol. Across Europe, for example, more than two-thirds do in most countries.

Again, the prevalence of drinking across North Africa and the Middle East is notably lower than elsewhere. Typically, 5 to 10 percent of adults across these regions drank in the preceding year, and in a number of countries, this was below 5 percent.

Alcohol consumption by sex When we look at gender differences, we see that in all countries, men are more likely to drink than women.

Data on the share who drink alcohol by gender and age group in the UK is available here.

Heavy drinking sessions Alcohol consumption – whilst a risk factor for a number of health outcomes – typically has the greatest negative impacts when consumed within heavy sessions.

This pattern of drinking is often termed 'binging,' where individuals consume large amounts of alcohol within a single session versus small quantities more frequently.

Heavy episodic drinking is defined as the proportion of adult drinkers who have had at least 60 grams or more of pure alcohol on at least one occasion in the past 30 days. An intake of 60 grams of pure alcohol is approximately equal to 6 standard alcoholic drinks.

The map shows heavy drinkers – those who had an episode of heavy drinking in the previous 30 days – as a share of total drinkers (i.e., those who have drunk less than one alcoholic drink in the last 12 months are excluded).

The comparison of this map with the previous maps makes clear that heavy drinking is not necessarily most common in the same countries where alcohol consumption is most common.

Data on the prevalence of binge drinking by age and gender in the UK can be found here, and trends in heavy and binge drinking in the USA can be found here.

Share of adults who don't drink alcohol Global trends on alcohol abstinence show a mirror image of drinking prevalence data. This is shown in the charts as the share of adults who had not drunk in the prior year and those who have never drunk alcohol.

Here, we see particularly high levels of alcohol abstinence across North Africa and the Middle East. In most countries in this region, the majority of adults have never drunk alcohol.

Global beer co...

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.

Contains covariate data for "The association between alcohol consumption per capita and suicide mortality across 30 European countries" which were extracted from the Pew Research Center (pewresearch.org), World Bank Group (worldbank.org), and Eurostat (ec.europa.eu/eurostat). Also contains dummy variables to represent: the 2008 global economic recession, changes from ICD-9 to ICD-10, and the COVID-19 pandemic. All covariates which were initially considered are included in this dataset. However, data were further cleaned according to methods described in the associated publication prior to analysis. Within the dataset: edu = Educational attainment (completion of post-secondary or equivalent) lit = Literacy, adult total (% of people ages 15 and above) unemp = Unemployment, total (% of total labor force) (modeled ILO estimate) divorce = Divorce rate migration = Net migration rate relig.muslim = Proportion of the population who identified as Muslim relig.buddhist = Proportion of the population who identified as Buddhist lff = Female labour force participation (% of total labor force) gdp = Gross domestic product based on purchasing power parity (GDP (PPP)) gini = Gini index density = Population density urban = Proportion of the population living in urban areas recession, covid, icd: Dummy variables detailed above.

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.

Alcohol is linked to many disease conditions and is one of the major risk factors for burden of disease in established market economies. The aim of this study was to calculate alcohol population attributable fractions for Scotland, using the best possible estimates based on the current evidence available in the epidemiological literature and specific estimates of population drinking in Scotland. These were then applied to mortality and morbidity data to estimate more fully the burden of alcohol related harm in Scotland. Source agency: ISD Scotland (part of NHS National Services Scotland) Designation: Official Statistics not designated as National Statistics Language: English Alternative title: Alcohol attributable mortality and morbidity: alcohol population attributable fractions for Scotland

EMSIndicators:The number of individual patients administered naloxone by EMSThe number of naloxone administrations by EMSThe rate of EMS calls involving naloxone administrations per 10,000 residentsData Source:The Vermont Statewide Incident Reporting Network (SIREN) is a comprehensive electronic prehospital patient care data collection, analysis, and reporting system. EMS reporting serves several important functions, including legal documentation, quality improvement initiatives, billing, and evaluation of individual and agency performance measures.Law Enforcement Indicators:The Number of law enforcement responses to accidental opioid-related non-fatal overdosesData Source:The Drug Monitoring Initiative (DMI) was established by the Vermont Intelligence Center (VIC) in an effort to combat the opioid epidemic in Vermont. It serves as a repository of drug data for Vermont and manages overdose and seizure databases. Notes:Overdose data provided in this dashboard are derived from multiple sources and should be considered preliminary and therefore subject to change. Overdoses included are those that Vermont law enforcement responded to. Law enforcement personnel do not respond to every overdose, and therefore, the numbers in this report are not representative of all overdoses in the state. The overdoses included are limited to those that are suspected to have been caused, at least in part, by opioids. Inclusion is based on law enforcement's perception and representation in Records Management Systems (RMS). All Vermont law enforcement agencies are represented, with the exception of Norwich Police Department, Hartford Police Department, and Windsor Police Department, due to RMS access. Questions regarding this dataset can be directed to the Vermont Intelligence Center at dps.vicdrugs@vermont.gov.Overdoses Indicators:The number of accidental and undetermined opioid-related deathsThe number of accidental and undetermined opioid-related deaths with cocaine involvementThe percent of accidental and undetermined opioid-related deaths with cocaine involvementThe rate of accidental and undetermined opioid-related deathsThe rate of heroin nonfatal overdose per 10,000 ED visitsThe rate of opioid nonfatal overdose per 10,000 ED visitsThe rate of stimulant nonfatal overdose per 10,000 ED visitsData Source:Vermont requires towns to report all births, marriages, and deaths. These records, particularly birth and death records are used to study and monitor the health of a population. Deaths are reported via the Electronic Death Registration System. Vermont publishes annual Vital Statistics reports.The Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) captures and analyzes recent Emergency Department visit data for trends and signals of abnormal activity that may indicate the occurrence of significant public health events.Population Health Indicators:The percent of adolescents in grades 6-8 who used marijuana in the past 30 daysThe percent of adolescents in grades 9-12 who used marijuana in the past 30 daysThe percent of adolescents in grades 9-12 who drank any alcohol in the past 30 daysThe percent of adolescents in grades 9-12 who binge drank in the past 30 daysThe percent of adolescents in grades 9-12 who misused any prescription medications in the past 30 daysThe percent of adults who consumed alcohol in the past 30 daysThe percent of adults who binge drank in the past 30 daysThe percent of adults who used marijuana in the past 30 daysData Sources:The Vermont Youth Risk Behavior Survey (YRBS) is part of a national school-based surveillance system conducted by the Centers for Disease Control and Prevention (CDC). The YRBS monitors health risk behaviors that contribute to the leading causes of death and disability among youth and young adults.The Behavioral Risk Factor Surveillance System (BRFSS) is a telephone survey conducted annually among adults 18 and older. The Vermont BRFSS is completed by the Vermont Department of Health in collaboration with the Centers for Disease Control and Prevention (CDC).Notes:Prevalence estimates and trends for the 2021 Vermont YRBS were likely impacted by significant factors unique to 2021, including the COVID-19 pandemic and the delay of the survey administration period resulting in a younger population completing the survey. Students who participated in the 2021 YRBS may have had a different educational and social experience compared to previous participants. Disruptions, including remote learning, lack of social interactions, and extracurricular activities, are likely reflected in the survey results. As a result, no trend data is included in the 2021 report and caution should be used when interpreting and comparing the 2021 results to other years.The Vermont Department of Health (VDH) seeks to promote destigmatizing and equitable language. While the VDH uses the term "cannabis" to reflect updated terminology, the data sources referenced in this data brief use the term "marijuana" to refer to cannabis. Prescription Drugs Indicators:The average daily MMEThe average day's supplyThe average day's supply for opioid analgesic prescriptionsThe number of prescriptionsThe percent of the population receiving at least one prescriptionThe percent of prescriptionsThe proportion of opioid analgesic prescriptionsThe rate of prescriptions per 100 residentsData Source:The Vermont Prescription Monitoring System (VPMS) is an electronic data system that collects information on Schedule II-IV controlled substance prescriptions dispensed by pharmacies. VPMS proactively safeguards public health and safety while supporting the appropriate use of controlled substances. The program helps healthcare providers improve patient care. VPMS data is also a health statistics tool that is used to monitor statewide trends in the dispensing of prescriptions.Treatment Indicators:The number of times a new substance use disorder is diagnosed (Medicaid recipients index events)The number of times substance use disorder treatment is started within 14 days of diagnosis (Medicaid recipients initiation events)The number of times two or more treatment services are provided within 34 days of starting treatment (Medicaid recipients engagement events)The percent of times substance use disorder treatment is started within 14 days of diagnosis (Medicaid recipients initiation rate)The percent of times two or more treatment services are provided within 34 days of starting treatment (Medicaid recipients engagement rate)The MOUD treatment rate per 10,000 peopleThe number of people who received MOUD treatmentData Source:Vermont Medicaid ClaimsThe Vermont Prescription Monitoring System (VPMS)Substance Abuse Treatment Information System (SATIS)

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