Tuberculosis is one of the deadliest communicable diseases worldwide, causing around *** million deaths per year. Communicable diseases, also known as infectious diseases, are spread from person to person either directly or indirectly, such as through an insect bite or ingesting contaminated food or water. Some of the deadliest communicable diseases include HIV/AIDS, malaria, hepatitis C, cholera, and measles. Tuberculosis Tuberculosis is an infectious disease that affects the lungs. Tuberculosis disproportionately impacts the poorer, less developed countries of the world, such as in Africa and Southeast Asia. India reports the highest number of deaths from tuberculosis worldwide. HIV/AIDS Although deaths from HIV/AIDS have decreased over the last few decades, there were still around ******* AIDS-related deaths in 2023. Like many other communicable diseases, HIV/AIDS impacts developing regions more than the developed world. By far, the highest number of AIDS deaths come from Africa and Asia Pacific. Advancements in HIV treatment now allow those infected to live long and relatively normal lives, but access to treatment varies greatly.

this graph was created in Tableu and Ourdataworld :

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fc5bb0b21c8b3a126eca89160e1d25d03%2Fgraph1.png?generation=1710708871099084&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Ff81fcfa72e97f08202ba1cb06fe138da%2Fgraph2.png?generation=1710708877558039&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fabbdfd146844a7e8d19e277c2eecb83b%2Fgraph3.png?generation=1710708883608541&alt=media" alt="">

Understanding the Global Distribution of HIV/AIDS Deaths

Introduction:

HIV/AIDS remains one of the most significant public health challenges globally, with its impact varying widely across countries and regions. While the overall share of deaths attributed to HIV/AIDS stands at around 1.5% globally, this statistic belies the stark disparities observed on a country-by-country basis. This essay delves into the global distribution of deaths from HIV/AIDS, examining both the overarching trends and the localized impacts across different regions, particularly focusing on Southern Sub-Saharan Africa.

Understanding Global Trends:

At a global level, HIV/AIDS accounts for approximately 1.5% of all deaths. This figure, though relatively low in comparison to other causes of mortality, represents a significant burden on public health systems and communities worldwide. However, when zooming in on specific regions, such as Europe, the share of deaths attributable to HIV/AIDS drops significantly, often comprising less than 0.1% of total mortality. This pattern suggests varying levels of prevalence and effectiveness of HIV/AIDS prevention and treatment strategies across different parts of the world.

Regional Disparities:

The distribution of HIV/AIDS deaths is not uniform across the globe, with certain regions experiencing disproportionately high burdens. Southern Sub-Saharan Africa emerges as a focal point of the HIV/AIDS epidemic, with a significant portion of deaths attributed to the virus occurring in this region. Factors such as limited access to healthcare, socio-economic disparities, cultural stigmatization, and insufficient education about HIV/AIDS contribute to the heightened prevalence and impact of the disease in this area.

Southern Sub-Saharan Africa: A Hotspot for HIV/AIDS Deaths:

Within Southern Sub-Saharan Africa, countries such as South Africa, Botswana, and Swaziland stand out for their exceptionally high rates of HIV/AIDS-related mortality. In these nations, HIV/AIDS can account for up to a quarter of all deaths, highlighting the acute nature of the epidemic in these regions. The reasons behind this disproportionate burden are multifaceted, encompassing issues ranging from inadequate healthcare infrastructure to socio-cultural barriers inhibiting prevention and treatment efforts.

Challenges and Responses:

Addressing the unequal distribution of HIV/AIDS deaths necessitates a multi-faceted approach that encompasses both prevention and treatment strategies tailored to the specific needs of affected communities. Efforts to expand access to antiretroviral therapy (ART), promote comprehensive sexual education, combat stigma, and strengthen healthcare systems are crucial components of an effective response. Moreover, fostering partnerships between governments, civil society organizations, and international entities is essential for coordinating resources and expertise to tackle the HIV/AIDS epidemic comprehensively.

Lessons Learned and Future Directions:

The global distribution of deaths from HIV/AIDS underscores the importance of context-specific interventions that take into account the unique social, economic, and cultural factors influencing the spread and impact of the disease. While progress has been made in reducing HIV/AIDS-related mortality in some regions, much work remains to be done, particularly in areas where the burden of the epidemic remains disproportionately high. Going forward, sustained investment in research, healthcare infrastructure, and community empowerment initiatives will be vital for achieving meaningful reductions in HIV/AIDS deaths worldwide.

Conclusion:

In conclusion, the global distribution of deaths from HIV/AIDS reveals a complex landscape characterized by both overarching trends and localized disparities. While the overall share of deaths attributable to HIV/AIDS may seem relatively modest on a global scale, the stark contrasts observed across different countries and regions underscore the need for targeted interventions tailored to the specific contexts in which the epidemic is most pronounced. By addressing the underlying social, economic, and healthcare-related factors driving the unequal distribution of HIV/AIDS deaths, the global co...

The average for 2022 based on 196 countries was 8.24 deaths per 1000 people. The highest value was in the Central African Republic: 55.13 deaths per 1000 people and the lowest value was in Qatar: 0.93 deaths per 1000 people. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

Real-time data on deaths per day worldwide

Diabetes continues to be a significant global health concern, with the Western Pacific region reporting the highest number of diabetes-related deaths in 2024, with around 1.2 million. This stark figure underscores the urgent need for improved diabetes prevention and management strategies worldwide. North America and the Caribbean followed with an estimated 526,000 deaths, while Africa is had the lowest number at 216,000. Regional disparities and global impact The prevalence of diabetes varies significantly across regions, reflecting differences in healthcare systems, lifestyle factors, and genetic predispositions. In the United States, the death rate from diabetes mellitus was 22.4 per 100,000 people in 2023, with 8.4 percent of the adult population living with the condition. Canada has seen a slight decrease in its diabetes-related death rate, falling from 21.8 per 100,000 in 2000 to 18.1 per 100,000 in 2023. These figures highlight the ongoing challenges in managing diabetes, even in countries with advanced healthcare systems. European landscape and global context Within Europe, Germany reported the highest number of diabetes-related deaths in 2024, with nearly 63,000 fatalities among adults aged 20 to 79 years. Italy followed closely with around 62,400 deaths. However, Czechia reported the highest mortality rates in Europe as of 2022, with 43.4 diabetes deaths per 100,000 population overall. On a global scale, diabetes remains a major health concern, with 19 percent of adults worldwide identifying it as one of the biggest health problems in their country.

The COVID-19 pandemic increased the global death rate, reaching *** in 2021, but had little to no significant impact on birth rates, causing population growth to dip slightly. On a global level, population growth is determined by the difference between the birth and death rates, known as the rate of natural change. On a national or regional level, migration also affects population change. Ongoing trends Since the middle of the 20th century, the global birth rate has been well above the global death rate; however, the gap between these figures has grown closer in recent years. The death rate is projected to overtake the birth rate in the 2080s, which means that the world's population will then go into decline. In the future, death rates will increase due to ageing populations across the world and a plateau in life expectancy. Why does this change? There are many reasons for the decline in death and birth rates in recent decades. Falling death rates have been driven by a reduction in infant and child mortality, as well as increased life expectancy. Falling birth rates were also driven by the reduction in child mortality, whereby mothers would have fewer children as survival rates rose - other factors include the drop in child marriage, improved contraception access and efficacy, and women choosing to have children later in life.

This dataset contains counts of deaths for California counties based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Number of deaths and mortality rates, by age group, sex, and place of residence, 1991 to most recent year.

Trends in Covid total deaths per million. The latest data for over 100 countries around the world.

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

Actual value and historical data chart for World Death Rate Crude Per 1 000 People

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

The average for 2022 based on 187 countries was 27 deaths per 1000 births. The highest value was in Niger: 121 deaths per 1000 births and the lowest value was in Estonia: 2 deaths per 1000 births. The indicator is available from 1960 to 2022. Below is a chart for all countries where data are available.

Canada CA: Mortality Rate: Under-5: Female: per 1000 Live Births data was reported at 4.700 Ratio in 2023. This stayed constant from the previous number of 4.700 Ratio for 2022. Canada CA: Mortality Rate: Under-5: Female: per 1000 Live Births data is updated yearly, averaging 7.000 Ratio from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 28.600 Ratio in 1960 and a record low of 4.700 Ratio in 2023. Canada CA: Mortality Rate: Under-5: Female: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank.WDI: Social: Health Statistics. Under-five mortality rate, female is the probability per 1,000 that a newborn female baby will die before reaching age five, if subject to female age-specific mortality rates of the specified year.;Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.;Weighted average;Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys. Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation. This is a sex-disaggregated indicator for Sustainable Development Goal 3.2.1 [https://unstats.un.org/sdgs/metadata/].

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update.

The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf

Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic.

Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More information on COVID-19 mortality can be found at the following link: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Mortality/Mortality-Statistics

Data are subject to future revision as reporting changes.

Starting in July 2020, this dataset will be updated every weekday.

Additional notes: A delay in the data pull schedule occurred on 06/23/2020. Data from 06/22/2020 was processed on 06/23/2020 at 3:30 PM. The normal data cycle resumed with the data for 06/23/2020.

A network outage on 05/19/2020 resulted in a change in the data pull schedule. Data from 5/19/2020 was processed on 05/20/2020 at 12:00 PM. Data from 5/20/2020 was processed on 5/20/2020 8:30 PM. The normal data cycle resumed on 05/20/2020 with the 8:30 PM data pull. As a result of the network outage, the timestamp on the datasets on the Open Data Portal differ from the timestamp in DPH's daily PDF reports.

Starting 5/10/2021, the date field will represent the date this data was updated on data.ct.gov. Previously the date the data was pulled by DPH was listed, which typically coincided with the date before the data was published on data.ct.gov. This change was made to standardize the COVID-19 data sets on data.ct.gov.

France FR: Mortality Rate Attributed to Unintentional Poisoning: Male: per 100,000 Male Population data was reported at 0.300 Ratio in 2016. This records a decrease from the previous number of 0.400 Ratio for 2015. France FR: Mortality Rate Attributed to Unintentional Poisoning: Male: per 100,000 Male Population data is updated yearly, averaging 0.400 Ratio from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 0.500 Ratio in 2000 and a record low of 0.300 Ratio in 2016. France FR: Mortality Rate Attributed to Unintentional Poisoning: Male: per 100,000 Male Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s France – Table FR.World Bank: Health Statistics. Mortality rate attributed to unintentional poisonings is the number of male deaths from unintentional poisonings in a year per 100,000 male population. Unintentional poisoning can be caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure.; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Kaggle Dataset Description

Title: Mortality Rate (Under-5, Per 1000 Live Births)
Subtitle: Exploring global trends in child survival and health advancements.

Detailed Description:
This dataset contains the under-5 mortality rate, measured as the number of deaths per 1,000 live births for children under five years of age. Sourced from the World Bank, it highlights progress in child survival and health outcomes globally over decades.

Key Highlights: - Annual data for countries worldwide. - Metric: Mortality rate (under-5, per 1000 live births). - Use cases: Analyze trends, compare regional disparities, and correlate mortality rates with health and economic indicators.

4. Exploratory Data Analysis (EDA)

Notebook Ideas

1. Data Cleaning:

   • Handle missing or inconsistent data.
   • Normalize data for comparison across regions.
   • Add calculated fields like regional averages or year-over-year changes.

2. Visualizations:

   • Line Graph: Trends in under-5 mortality rates over time for selected countries.
   • Heatmap: Mortality rates by region and year.
   • Scatterplot: Correlation between mortality rates and healthcare expenditure or GDP per capita.
   • Bar Chart: Top and bottom countries by under-5 mortality for a specific year.

3. Descriptive Analysis:

   • Highlight countries with the most significant reductions in mortality.
   • Analyze regional improvements over decades (e.g., Sub-Saharan Africa vs. South Asia).

5. Predictive Analysis (Optional)

• Use time-series forecasting (e.g., ARIMA or Prophet) to predict future mortality rates for specific countries or regions.
• Explore regression models to analyze the impact of factors like healthcare expenditure on mortality reduction.

6. Kaggle Notebook

Create a Kaggle notebook with: 1. Data Cleaning: Show how missing or inconsistent values are handled. 2. EDA: Include visualizations like heatmaps, scatterplots, and line charts. 3. Insights: Highlight significant findings, such as countries with notable improvements in child survival. 4. Optional Predictive Modeling: Use regression or time-series models to project future trends.

7. Call to Action

For GitHub:

• Share the GitHub repository link on LinkedIn, Twitter, and forums like Reddit (e.g., r/datascience).
• Invite collaboration:
  • "Fork this repository to add your analyses or insights!"

GitHub Link: https://github.com/yourusername/Under5_Mortality_Trends

For Kaggle:

• Encourage upvotes:
  • "If this dataset helps you, consider upvoting it to help others discover it!"
• Include questions to engage users:
  • "Which regions have made the most progress in reducing child mortality?"
  • "What correlations can be drawn between healthcare expenditure and mortality rates?"

Kaggle Link: https://www.kaggle.com/datasets/yourusername/under5-mortality-rate

8. LinkedIn Post

Post Title:
📉 Global Trends in Under-5 Mortality Rates 🌍

Post Body:
I’m excited to share my latest dataset on under-5 mortality rates (per 1,000 live births), sourced from the World Bank. This dataset highlights progress in global health and child survival, spanning decades and covering countries worldwide.

📂 Explore the Dataset:
- GitHub Repository: https://github.com/yourusername/Under5_Mortality_Trends
- Kaggle Dataset: https://www.kaggle.com/datasets/yourusername/under5-mortality-rate

Why It Matters:

Child survival is a fundamental measure of global health progress. This dataset is ideal for:
- Trend Analysis: Explore how under-5 mortality rates have evolved globally.
- Regional Comparisons: Identify disparities in child survival rates across regions.
- Correlations: Study the relationship between mortality rates and economic indicators like healthcare expenditure or GDP per capita.

📈 Get Involved:
- Use the dataset for your own analyses and visualizations.
- Share your insights and findings.
- Upvote the Kaggle dataset to help others discover it!

❓ What trends or correlations do you find in the data?
- Which country or region has shown the most improvement?
- What factors would you analyze further?

Let me know your thoughts, and feel free to share this resource with others who might benefit! 🌟

DataScience #ChildHealth #MortalityRates #WorldBankData #DataVisualization #GitHub #Kaggle #HealthAnalysis

Let me know if you'd like assistance with EDA or visualization templates!

The deadliest energy source worldwide is coal. It is estimated that there are roughly 33 deaths from brown coal (also known as Lignite) and 25 deaths from coal per terawatt-hour (TWh) of electricity produced from these fossil fuels. While figures take into account accidents, the majority of deaths associated with coal come from air pollution. Air pollution deaths from fossil fuels Air pollution from coal-fired plants has been of growing concern as it has been linked to asthma, cancer, and heart disease. Burning coal can release toxic airborne pollutants such as mercury, sulfur dioxide, nitrogen oxides, and particulate matter. Eastern Asia accounts for roughly 31 percent of global deaths attributable to exposure to fine particulate matter (PM2.5) generated by fossil fuel combustion, which is perhaps unsurprising given the fact China and India are the two largest coal consumers in the world. Safest energy source Clean and renewable energy sources are unsurprisingly the least deadly energy sources, with 0.04 and 0.02 deaths associated with wind and solar per unit of electricity, respectively. Nuclear energy also has a low death rate, even after the inclusion of nuclear catastrophes like Chernobyl and Fukushima.

This dataset provides values for CORONAVIRUS DEATHS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

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
• ...