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

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.

Context

A straightforward way to assess the health status of a population is to focus on mortality – or concepts like child mortality or life expectancy, which are based on mortality estimates. A focus on mortality, however, does not take into account that the burden of diseases is not only that they kill people, but that they cause suffering to people who live with them. Assessing health outcomes by both mortality and morbidity (the prevalent diseases) provides a more encompassing view on health outcomes. This is the topic of this entry. The sum of mortality and morbidity is referred to as the ‘burden of disease’ and can be measured by a metric called ‘Disability Adjusted Life Years‘ (DALYs). DALYs are measuring lost health and are a standardized metric that allow for direct comparisons of disease burdens of different diseases across countries, between different populations, and over time. Conceptually, one DALY is the equivalent of losing one year in good health because of either premature death or disease or disability. One DALY represents one lost year of healthy life. The first ‘Global Burden of Disease’ (GBD) was GBD 1990 and the DALY metric was prominently featured in the World Bank’s 1993 World Development Report. Today it is published by both the researchers at the Institute of Health Metrics and Evaluation (IHME) and the ‘Disease Burden Unit’ at the World Health Organization (WHO), which was created in 1998. The IHME continues the work that was started in the early 1990s and publishes the Global Burden of Disease study.

Content

In this Dataset, we have Historical Data of different cause of deaths for all ages around the World. The key features of this Dataset are: Meningitis, Alzheimer's Disease and Other Dementias, Parkinson's Disease, Nutritional Deficiencies, Malaria, Drowning, Interpersonal Violence, Maternal Disorders, HIV/AIDS, Drug Use Disorders, Tuberculosis, Cardiovascular Diseases, Lower Respiratory Infections, Neonatal Disorders, Alcohol Use Disorders, Self-harm, Exposure to Forces of Nature, Diarrheal Diseases, Environmental Heat and Cold Exposure, Neoplasms, Conflict and Terrorism, Diabetes Mellitus, Chronic Kidney Disease, Poisonings, Protein-Energy Malnutrition, Road Injuries, Chronic Respiratory Diseases, Cirrhosis and Other Chronic Liver Diseases, Digestive Diseases, Fire, Heat, and Hot Substances, Acute Hepatitis.

Dataset Glossary (Column-wise)

• 01. Country/Territory - Name of the Country/Territory
• 02. Code - Country/Territory Code
• 03. Year - Year of the Incident
• 04. Meningitis - No. of People died from Meningitis
• 05. Alzheimer's Disease and Other Dementias - No. of People died from Alzheimer's Disease and Other Dementias
• 06. Parkinson's Disease - No. of People died from Parkinson's Disease
• 07. Nutritional Deficiencies - No. of People died from Nutritional Deficiencies
• 08. Malaria - No. of People died from Malaria
• 09. Drowning - No. of People died from Drowning
• 10. Interpersonal Violence - No. of People died from Interpersonal Violence
• 11. Maternal Disorders - No. of People died from Maternal Disorders
• 12. Drug Use Disorders - No. of People died from Drug Use Disorders
• 13. Tuberculosis - No. of People died from Tuberculosis
• 14. Cardiovascular Diseases - No. of People died from Cardiovascular Diseases
• 15. Lower Respiratory Infections - No. of People died from Lower Respiratory Infections
• 16. Neonatal Disorders - No. of People died from Neonatal Disorders
• 17. Alcohol Use Disorders - No. of People died from Alcohol Use Disorders
• 18. Self-harm - No. of People died from Self-harm
• 19. Exposure to Forces of Nature - No. of People died from Exposure to Forces of Nature
• 20. Diarrheal Diseases - No. of People died from Diarrheal Diseases
• 21. Environmental Heat and Cold Exposure - No. of People died from Environmental Heat and Cold Exposure
• 22. Neoplasms - No. of People died from Neoplasms
• 23. Conflict and Terrorism - No. of People died from Conflict and Terrorism
• 24. Diabetes Mellitus - No. of People died from Diabetes Mellitus
• 25. Chronic Kidney Disease - No. of People died from Chronic Kidney Disease
• 26. Poisonings - No. of People died from Poisoning
• 27. Protein-Energy Malnutrition - No. of People died from Protein-Energy Malnutrition
• 28. Chronic Respiratory Diseases - No. of People died from Chronic Respiratory Diseases
• 29. Cirrhosis and Other Chronic Liver Diseases - No. of People died from Cirrhosis and Other Chronic Liver Diseases
• 30. Digestive Diseases - No. of People died from Digestive Diseases
• 31. Fire, Heat, and Hot Substances - No. of People died from Fire or Heat or any Hot Substances
• ...

About Dataset

Context:

A straightforward way to assess the health status of a population is to focus on mortality – or concepts like child mortality or life expectancy, which are based on mortality estimates. A focus on mortality, however, does not take into account that the burden of diseases is not only that they kill people, but that they cause suffering to people who live with them. Assessing health outcomes by both mortality and morbidity (the prevalent diseases) provides a more encompassing view on health outcomes. This is the topic of this entry. The sum of mortality and morbidity is referred to as the ‘burden of disease’ and can be measured by a metric called ‘Disability Adjusted Life Years‘ (DALYs).

DALYs are measuring lost health and are a standardized metric that allow for direct comparisons of disease burdens of different diseases across countries, between different populations, and over time. Conceptually, one DALY is the equivalent of losing one year in good health because of either premature death or disease or disability. One DALY represents one lost year of healthy life. The first ‘Global Burden of Disease’ (GBD) was GBD 1990 and the DALY metric was prominently featured in the World Bank’s 1993 World Development Report. Today it is published by both the researchers at the Institute of Health Metrics and Evaluation (IHME) and the ‘Disease Burden Unit’ at the World Health Organization (WHO), which was created in 1998. The IHME continues the work that was started in the early 1990s and publishes the Global Burden of Disease study.

Content:

In this Dataset, we have Historical Data of different cause of deaths for all ages around the World. The key features of this Dataset are: Meningitis, Alzheimer's Disease and Other Dementias, Parkinson's Disease, Nutritional Deficiencies, Malaria, Drowning, Interpersonal Violence, Maternal Disorders, HIV/AIDS, Drug Use Disorders, Tuberculosis, Cardiovascular Diseases, Lower Respiratory Infections, Neonatal Disorders, Alcohol Use Disorders, Self-harm, Exposure to Forces of Nature, Diarrheal Diseases, Environmental Heat and Cold Exposure, Neoplasms, Conflict and Terrorism, Diabetes Mellitus, Chronic Kidney Disease, Poisonings, Protein-Energy Malnutrition, Road Injuries, Chronic Respiratory Diseases, Cirrhosis and Other Chronic Liver Diseases, Digestive Diseases, Fire, Heat, and Hot Substances, Acute Hepatitis.

Dataset Glossary (Column-wise):

1. Country/Territory - Name of the Country/Territory
2. Code - Country/Territory Code
3. Year - Year of the Incident
4. Meningitis - No. of People died from Meningitis
5. Alzheimer's Disease and Other Dementias - No. of People died from Alzheimer's Disease and Other Dementias
6. Parkinson's Disease - No. of People died from Parkinson's Disease
7. Nutritional Deficiencies - No. of People died from Nutritional Deficiencies
8. Malaria - No. of People died from Malaria
9. Drowning - No. of People died from Drowning
10. Interpersonal Violence - No. of People died from Interpersonal Violence
11. Maternal Disorders - No. of People died from Maternal Disorders
12. Drug Use Disorders - No. of People died from Drug Use Disorders
13. Tuberculosis - No. of People died from Tuberculosis
14. Cardiovascular Diseases - No. of People died from Cardiovascular Diseases
15. Lower Respiratory Infections - No. of People died from Lower Respiratory Infections
16. Neonatal Disorders - No. of People died from Neonatal Disorders
17. Alcohol Use Disorders - No. of People died from Alcohol Use Disorders
18. Self-harm - No. of People died from Self-harm
19. Exposure to Forces of Nature - No. of People died from Exposure to Forces of Nature
20. Diarrheal Diseases - No. of People died from Diarrheal Diseases
21. Environmental Heat and Cold Exposure - No. of People died from Environmental Heat and Cold Exposure
22. Neoplasms - No. of People died from Neoplasms
23. Conflict and Terrorism - No. of People died from Conflict and Terrorism
24. Diabetes Mellitus - No. of People died from Diabetes Mellitus
25. Chronic Kidney Disease - No. of People died from Chronic Kidney Disease
26. Poisonings - No. of People died from Poisoning
27. Protein-Energy Malnutrition - No. of People died from Protein-Energy Malnutrition
28. Chronic Respiratory Diseases - No. of People died from Chronic Respiratory Diseases
29. Cirrhosis and Other Chronic Liver Diseases - No. of People died from Cirrhosis and Other Chronic Liver Diseases
30. Digestive Diseases - No. of People died from Digestive Diseases
31. Fire, Heat, and Hot Substances - No. of People died from Fire or Heat or any Hot Substances
32. Acute Hepatitis - No. of People died from Acute Hepatitis Structure of the Dataset

Acknowledgement:

This Dataset is created from Our World in Data. This Dataset falls under open access under the Creative Commons BY license. You can check the FAQ for more informa...

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 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 presents the rate of mortality among adults receiving treatment for alcohol misuse within local authorities. It provides a measure of health outcomes for individuals engaged in alcohol treatment services and supports public health monitoring and service improvement. The indicator is expressed as a crude mortality rate per 1,000 individuals in treatment.

Rationale

The rationale for this indicator is to monitor the mortality risk among adults in alcohol treatment. Understanding these rates helps assess the effectiveness and safety of treatment services and identify areas where additional support or intervention may be needed.

Numerator

The numerator is the number of deaths among adults receiving alcohol treatment within a given local authority.

Denominator

The denominator is the total number of adults in alcohol treatment in the same local authority.

Caveats

This indicator is presented as a crude mortality rate per 1,000 individuals, which differs from the version published on OHID’s Fingertips platform, where it is expressed as a mortality ratio. Users should be cautious when comparing figures across sources due to these methodological differences.

External references

OHID Fingertips: Deaths in Alcohol Treatment

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

This dataset is a more different and reliable version to KumarRajarshi's Life Expectancy (WHO) dataset - where some of his values and methods can be questioned.

Context All of the data in this dataset is compiled and downloaded from the Global Health Observatory (GHO) – which is a public health data repository established by the World Health Organisation (WHO). This makes the dataset very reliable and valid.

Challenges - Perform EDA to explore factors that affect life expectancy? - Produce a model to predict life expectancy?

Dataset Contents Life Expectancy from birth: - https://www.who.int/data/gho/data/indicators/indicator-details/GHO/life-expectancy-at-birth-(years)

Mean BMI (kg/m²) (crude estimate): - https://www.who.int/data/gho/data/indicators/indicator-details/GHO/mean-bmi-(kg-m-)-(crude-estimate)

Alcohol, total per capita (15+) consumption (in litres of pure alcohol): - https://www.who.int/data/gho/data/indicators/indicator-details/GHO/total-(recorded-unrecorded)-alcohol-per-capita-(15-)-consumption

The rest of the factors: - https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death (BY COUNTRY, Summary tables of mortality estimates by cause, age and sex, by country, 2000–2019, Number of Deaths [2000, 2010, 2015, 2019]). All of the values are crude estimates number of deaths per 1000.

I did this so you don't have to!

Data Collected: March 2023

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)

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.

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."

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Unveiling the Health and Demographics Dataset: Illuminating Life Expectancy

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

Discover the Gems in Our Dataset:

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

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

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

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

This dataset presents the age-standardised mortality rate from drug misuse across the population. It captures deaths where the underlying cause is linked to mental and behavioural disorders due to psychoactive substance use (excluding alcohol, tobacco, and volatile solvents), as well as deaths involving poisoning by controlled drugs. The data is sourced from the Office for National Statistics (ONS) and is intended to support public health monitoring and policy development aimed at reducing drug-related harm.

Rationale The indicator is designed to track and reduce the mortality rate from drug misuse. Monitoring these deaths helps inform public health strategies, resource allocation, and interventions aimed at preventing drug-related harm and supporting individuals with substance use disorders.

Numerator The numerator includes deaths where the underlying cause is coded to specific categories of mental and behavioural disorders due to psychoactive substance use (excluding alcohol, tobacco, and volatile solvents), as well as deaths involving poisoning by drugs controlled under the Misuse of Drugs Act 1971. These include accidental, intentional, undetermined, and assault-related poisonings, as well as disorders due to volatile solvents.

Denominator The denominator is the total population of the relevant age group, as recorded in the 2021 Census.

Caveats There are limitations in the classification and reporting of drug-related deaths, including potential underreporting or misclassification in death records. The indicator may not capture all deaths indirectly related to drug misuse, and changes in coding practices or legal definitions over time may affect comparability.

External references Public Health England - Fingertips: Deaths from drug misuse

Localities ExplainedThis dataset contains data based on either the resident locality or registered locality of the patient, a distinction is made between resident locality and registered locality populations:Resident Locality refers to individuals who live within the defined geographic boundaries of the locality. These boundaries are aligned with official administrative areas such as wards and Lower Layer Super Output Areas (LSOAs).Registered Locality refers to individuals who are registered with GP practices that are assigned to a locality based on the Primary Care Network (PCN) they belong to. These assignments are approximate—PCNs are mapped to a locality based on the location of most of their GP surgeries. As a result, locality-registered patients may live outside the locality, sometimes even in different towns or cities.This distinction is important because some health indicators are only available at GP practice level, without information on where patients actually reside. In such cases, data is attributed to the locality based on GP registration, not residential address.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

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.

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."

ObjectiveAlcohol misuse is a well-known risk factor for suicide however, the relationship between alcohol-related hospital admission and subsequent risk of death from suicide is unknown. We aimed to determine the risk of death from suicide following emergency admission to hospital with an alcohol-related cause.MethodsWe established an electronic cohort study of all 2,803,457 residents of Wales, UK, aged from 10 to under 100 years on 1 January 2006 with six years’ follow-up. The outcome event was death from suicide defined as intentional self-harm (ICD-10 X60-84) or undetermined intent (Y10-34). The main exposure was an alcohol-related admission defined as a ‘wholly attributable’ ICD-10 alcohol code in the admission record. Admissions were coded for the presence or absence of co-existing psychiatric morbidity. The analysis was by Cox regression with adjustments for confounding variables within the dataset.ResultsDuring the study follow-up period, there were 15,546,355 person years at risk with 28,425 alcohol-related emergency admissions and 1562 suicides. 125 suicides followed an admission (144.6 per 100,000 person years), of which 11 (9%) occurred within 4 weeks of discharge. The overall adjusted hazard ratio (HR) for suicide following admission was 26.8 (95% confidence interval (CI) 18.8 to 38.3), in men HR 9.83 (95% CI 7.91 to 12.2) and women HR 28.5 (95% CI 19.9 to 41.0). The risk of suicide remained substantial in subjects without known co-existing psychiatric morbidity: HR men 8.11 (95% CI 6.30 to 10.4) and women HR 24.0 (95% CI 15.5 to 37.3). The analysis was limited by the absence in datasets of potentially important confounding variables and the lack of information on alcohol-related harm and psychiatric morbidity in subjects not admitted to hospital.ConclusionEmergency alcohol-related hospital admission is associated with an increased risk of suicide. Identifying individuals in hospital provides an opportunity for psychosocial assessment and suicide prevention of a targeted at-risk group before their discharge to the community.

Liver disease is the greatest cause of death related to alcohol and a major public health problem. While excessive alcohol intake results in hepatosteatosis in most individuals, this can progress in some to more severe forms of liver disease including fibrosis and cirrhosis. An ongoing challenge in the management of alcoholic liver disease is the identification of liver injury early in the disease process such that intervention strategies can prevent serious long term outcomes. Given that excessive alcohol consumption results in dysregulation of lipid metabolism we applied lipid profiling technology to characterise and compare serum lipid profiles from excessive chronic drinkers with no liver disease to those with advanced alcoholic cirrhosis. In a cohort of 59 excessive drinkers (31 with liver cirrhosis and 28 with no evidence of liver disease) we used electrospray ionisation tandem mass spectrometry to measure over 300 individual lipid species in serum, including species of the major phospholipid, sphingolipid, glycerolipid and sterol classes. Six of the 25 lipid classes and subclasses were significantly associated with alcoholic liver cirrhosis; these included dihexosylceramide, trihexosylceramide, alkylphosphatidylcholine, lysoalkylphosphatidylcholine, phosphatidylinositol and free cholesterol. Multivariate classification models created with only clinical characteristics gave an optimal model with an AUC of 0.847 and an accuracy of 79.7%. The addition of lipid measurements to the clinical characteristics resulted in models of improved performance with an AUC of 0.892 and accuracy of 81.8%. The gain in AUC and accuracy of the combined models highlight the potential of serum lipids as markers of liver injury in alcoholic liver disease.

A number of world literature reports indicate that a latent Toxoplasma gondii infection leads to development of central nervous system disorders, which in turn may lead to altered behavior in the affected individuals. T. gondii infection has been observed to play the greatest role in drivers, suicides, and psychiatric patients. Studies conducted for this manuscript involve a different, never before really reported correlation between latent T. gondii infection and ethanol abuse. A total of 538 decedents with a known cause of death were included in the study. These individuals were divided into three groups: the risky behavior group, inconclusively risky behavior group, and control group. The criterion for this division was the likely effect of the individual’s behavior on the mechanism and cause of his/her death. The material used for analyses were blood samples collected during routine medico-legal examinations in these cases. The blood samples were used to measure anti-Toxoplasma IgG antibodies with an enzyme-linked immunosorbent assay (ELISA). Moreover, the following data were recorded for each decedent: sex, age, circumstances of death, cause of death, time from death to autopsy, and (if provided) substance abuse status (alcohol, illicit drugs). In those cases where blood alcohol level or toxicology tests were requested by the Prosecutor’s Office, their results were also included in our analysis. Test results demonstrated a strong correlation between chronic Toxoplasma gondii infestation and engaging in risky behaviors leading to death. Moreover, analyses demonstrated a positive correlation between the presence of anti-T. gondii IgG antibodies and psychoactive substance (especially ethanol) abuse, however, the causal relationship remains unclear. Due to the fact that alcohol abuse constitutes a significant social problem, searching for eliminable risk factors for dependence is extremely important. Our analyses provided new important information on the possible effects of latent T. gondii infection in humans.

Unselected population-based nationwide studies on the excess mortality of individuals with severe mental disorders are scarce with regard to several important causes of death. Using comprehensive register data, we set out to examine excess mortality and its trends among patients with severe mental disorders compared to the total population. Patients aged 25–74 and hospitalised with severe mental disorders in 1990–2010 in Finland were identified using the national hospital discharge register and linked individually to population register data on mortality and demographics. We studied mortality in the period 1996–2010 among patients with psychotic disorders, psychoactive substance use disorders, and mood disorders by several causes of death. In addition to all-cause mortality, we examined mortality amenable to health care interventions, ischaemic heart disease mortality, disease mortality, and alcohol-related mortality. Patients with severe mental disorders had a clearly higher mortality rate than the total population throughout the study period regardless of cause of death, with the exception of alcohol-related mortality among male patients with psychotic disorders without comorbidity with substance use disorders. The all-cause mortality rate ratio of patients with psychotic disorders compared to the total population was 3.48 (95% confidence interval 2.98–4.06) among men and 3.75 (95% CI 3.08–4.55) among women in the period 2008–10. The corresponding rate ratio of patients with psychoactive substance use disorders was 5.33 (95% CI 4.87–5.82) among men and 7.54 (95% CI 6.30–9.03) among women. Overall, the mortality of the total population and patients with severe mental disorders decreased between 1996 and 2010. However, the mortality rate ratio of patients with psychotic disorders and patients with psychoactive substance use disorders compared to the total population increased in general during the study period. Exceptions were alcohol-related mortality among patients with psychoactive substance use disorders and female patients with psychotic disorders, as well as amenable mortality among male patients with psychotic disorders. The mortality rate ratio of persons with mood disorders compared to the total population decreased. The markedly high mortality amenable to health care intervention among patients with severe mental disorders found in our study suggests indirectly that they may receive poorer quality somatic care. The results highlight the challenges in co-ordinating mental and somatic health services.

During the first two year of the Covid-19 pandemic, deaths tolls differed from a country to another. In a previous research work on 39 countries, we have found that some population’s characteristics were either negatively (birth rate/mortality rate, fertility rate) or positively (cancer score, Alzheimer disease score, percent of people above 65 years old, levels of alcohol intake) correlated with Covid-19 mortality. We also found that low levels of climate factors (average annual temperature, average hours of sunshine, average annual level of UV index) were positively correlated with Covid-19 deaths numbers as well. In the present study, we have developped an anti-Covid Capacity index that takes into account all the above mentioned parameters. The polynomial analysis of the anti-Covid Capacity and its corresponding geographic latitude of each country has generated a bell-shaped curve, with a high coefficient of determination (R2= 0.78). Lower anti-Covid capacity values were recorded in countries of low and high latitudes, respectively. Instead, plotting covid-19 deaths numbers against geographic latitude levels has generated an inverted bell-shaped curve, with higher deaths numbers at low and high latitudes, respectively. The analysis by a simple linear regression has shown that Covid-19 deaths numbers were significantly (p= 2,40 x 10-9) and negatively correlated to the anti-Covid Capacity index values. Our data demonstrate that the negative prepandemic human conditions, and the low scores of both annual temperature and UV index in many countries were the key factors behind high Covid-19 mortality, and they can be expressed as a simple index of anti-Covid capacity of a country that can predict the death-associated severity of Covid-19 disease, and thus, according to a country’s geographic latitude.