Introduction

This dataset contains death rate of different countries from 1960 to 2019.

Acknowledgement

Source: https://data.worldbank.org/indicator/SP.DYN.CDRT.IN

This statistic depicts the mean tuberculosis death rates in high-burden countries worldwide in 2019, per 100,000 population. The Central African Republic led the ranking that year with a mean mortality rate of about ** per 100,000 population.

Dear Kaggler! This dataset consists of a main CSV file: Adult mortality rate (2019-2021).csv. This file has been processed, cleaned and prepared for your use. The dataset contains information on mortality rates in different countries of the world and some factors that may affect this rate for 2019-2023.

The data contains the following columns:

Countries: Country of study.

Continent: Continent location of the country.

Average_Pop(thousands people): Average population of the country under study for 2019-2021 in thousands.

Average_GDP(M$): Average GDP of the country under study for 2019-2021 in millions of dollars.

Average_GDP_per_capita: Average GDP per capita of the country under study for 2019-2021 in dollars.

Average_HEXP($): Health Expenditure Per Capita in the country under study in dollars.

Development_level: Level of development of the state under study (calculated by GDP per capita of the country). Please note that in this dataset we calculate this indicator only by calculating GDP per capita! Despite the fact that the United Nations (UN) does not have an unambiguous classification of countries into developed, developing and backward based on only one indicator, such as the amount of GDP per capita. It uses a wider range of economic, social and quality indicators to determine the level of development of countries.

AMR_female(per_1000_female_adults): Average mortality of adult women in the country under study (per 1000 adult women per year) for 2019-2023.

AMR_male(per_1000_male_adults): Average mortality of adult men in the country under study (per 1000 adult men per year) for 2019-2023.

Average_CDR: Average crude mortality rate for 2019–2021 in the country under study.

The dataset also contains additional files: Draft_AMR.csv, Draft_CDR.csv, Draft_Expenses.csv, Draft_GDP.csv, Draft_Population.csv. In fact, the main dataset consists of parts of these files. If you are interested in working more deeply on data cleaning and preparation, you can of course use these files. You can also use these files to create your own dataset. And be careful! Additional files may contain a different number of rows and columns with different names and data types. And of course these files are not cleaned. You will see not only the NaN values, but also other symbols in their place.

Enjoy your training, my dear Kaggler!

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16192307%2Fc9a98b25b85b43718e5b8109712ba36a%2FDepositphotos_68536025_s-2019.jpg?generation=1711099905559419&alt=media" alt="">

As of 2023, the countries with the highest death rates worldwide were Monaco, Bulgaria, and Latvia. In these countries, there were ** to ** deaths per 1,000 people. The country with the lowest death rate is Qatar, where there is just *** death per 1,000 people. Leading causes of death The leading causes of death worldwide are, by far, cardiovascular diseases, accounting for ** percent of all deaths in 2021. That year, there were **** million deaths worldwide from ischaemic heart disease and **** million from stroke. Interestingly, a worldwide survey from that year found that people greatly underestimate the proportion of deaths caused by cardiovascular disease, but overestimate the proportion of deaths caused by suicide, interpersonal violence, and substance use disorders. Death in the United States In 2023, there were around **** million deaths in the United States. The leading causes of death in the United States are currently heart disease and cancer, accounting for a combined ** percent of all deaths in 2023. Lung and bronchus cancer is the deadliest form of cancer worldwide, as well as in the United States. In the U.S. this form of cancer is predicted to cause around ****** deaths among men alone in the year 2025. Prostate cancer is the second-deadliest cancer for men in the U.S. while breast cancer is the second deadliest for women. In 2023, the tenth leading cause of death in the United States was COVID-19. Deaths due to COVID-19 resulted in a significant rise in the total number of deaths in the U.S. in 2020 and 2021 compared to 2019, and it was the third leading cause of death in the U.S. during those years.

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.

COVID-19 rate of death, or the known deaths divided by confirmed cases, was over ten percent in Yemen, the only country that has 1,000 or more cases. This according to a calculation that combines coronavirus stats on both deaths and registered cases for 221 different countries. Note that death rates are not the same as the chance of dying from an infection or the number of deaths based on an at-risk population. By April 26, 2022, the virus had infected over 510.2 million people worldwide, and led to a loss of 6.2 million. 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.

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. Note that Statista aims to also provide domestic source material for a more complete picture, and not to just look at one particular source. Examples are these statistics on the confirmed coronavirus cases in Russia or the COVID-19 cases in Italy, both of which are from domestic sources. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

A word on the flaws of numbers like this

People are right to ask whether these numbers are at all representative or not for several reasons. First, countries worldwide decide differently on who gets tested for the virus, meaning that comparing case numbers or death rates could to some extent be misleading. Germany, for example, started testing relatively early once the country’s first case was confirmed in Bavaria in January 2020, whereas Italy tests for the coronavirus postmortem. Second, not all people go to see (or can see, due to testing capacity) a doctor when they have mild symptoms. Countries like Norway and the Netherlands, for example, recommend people with non-severe symptoms to just stay at home. This means not all cases are known all the time, which could significantly alter the death rate as it is presented here. Third and finally, numbers like this change very frequently depending on how the pandemic spreads or the national healthcare capacity. It is therefore recommended to look at other (freely accessible) content that dives more into specifics, such as the coronavirus testing capacity in India or the number of hospital beds in the UK. Only with additional pieces of information can you get the full picture, something that this statistic in its current state simply cannot provide.

Population: Death Rate: Yunnan data was reported at 0.861 % in 2023. This records an increase from the previous number of 0.821 % for 2022. Population: Death Rate: Yunnan data is updated yearly, averaging 0.725 % from Dec 1990 (Median) to 2023, with 34 observations. The data reached an all-time high of 0.861 % in 2023 and a record low of 0.620 % in 2019. Population: Death Rate: Yunnan data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Death Rate: By Region.

In 2019, Bolivia, Chile, and the Dominican Republic had the highest number of deaths per 100,000 population due to hepatitis B in Latin America and the Caribbean, with an estimate of *** deaths per 100,000 people. Guyana followed, with around *** deaths from hepatitis B per 100,000 individuals. Hepatitis B is a liver infection produced by the hepatitis B virus (HBV).

Dataset Description

This dataset serves as a comprehensive repository of global development metrics, consolidating data from multiple international organizations into a single, unified structure. It provides a granular view of the state of health, economy, and nutrition across 193 countries over a 30-year period (1990–2019).

The data is organized by Country, Year, and Gender (Male, Female, and Both Sexes), making it a valuable resource for longitudinal studies, demographic analysis, and socio-economic research. It combines high-level economic indicators (like GDP) with granular health metrics (specific mortality rates) and detailed nutritional breakdowns (diet composition by food group).

Content Overview

The dataset covers a wide spectrum of categories:

• Demographics & Economy: Population stats, GNI, GDP, and poverty rates.
• Mortality & Life Expectancy: Survival rates at various ages, maternal mortality, and life expectancy.
• Public Health: Incidence of infectious diseases (Malaria, Tuberculosis, Hepatitis B) and prevalence of health risks (Tobacco, road traffic accidents).
• Environmental Health: Mortality attributed to air pollution, sanitation access, and clean fuel availability.
• Nutrition: Detailed caloric and quantity breakdown of food consumption (fruits, vegetables, cereals, meats, etc.).
• Healthcare Infrastructure: Coverage of essential health services and density of medical professionals.

Sources

The data was extracted and unified via an ETL process from the following organizations:

• World Bank: World Development Indicators
• WHO: Global Health Observatory Data
• UNICEF: Data Warehouse
• Our World In Data: Statistics and Research
• FAOSTAT: Food and Agriculture Data
• Kaggle: WHO World Health Statistics 2020 Complete

Data Dictionary

Index Columns

• Country: Name of the country.
• Year: The calendar year of the recorded data.
• Gender: The gender category for the data (Female, Male, or Both sexes).

Demographics & Health Metrics

• Life Expectancy: The average number of years a newborn is expected to live.
• Infant Mortality Rate: Number of infants dying before reaching one year of age, per 1,000 live births.
  • Includes Low/High Confidence Interval (CI) columns.
• Under 5 Mortality Rate: Probability of a child dying before reaching age 5, per 1,000 live births.
  • Includes Low/High CI columns.
• Neonatal Mortality Rate: Number of deaths during the first 28 days of life per 1,000 live births.
  • Includes Low/High CI columns.
• Maternal Mortality Ratio: Number of maternal deaths due to childbirth per 100,000 live births.
  • Includes Low/High CI columns.
• Birth Rate: Number of births per 1,000 inhabitants.
• Death Rate: Number of deaths per 1,000 inhabitants.
• Adolescent Birth Rate: Number of births by women aged 15 to 19 per 1,000 women in that age range.
• % Population Aged 0-14 / 15-64 / 65+: Percentage of the total population falling into these specific age brackets.
• % Population Aged 65-69 / 70-74 / 75-79 / 80+: Granular breakdown of the elderly population percentages.
• Total Population: Total number of inhabitants.

Causes of Death & Disease

• % Death Cardiovascular: Probability of dying from cardiovascular diseases, cancer, diabetes, or chronic respiratory diseases between ages 30 and 70.
  • Includes Low/High CI columns.
• Incidence of Malaria: Number of malaria cases per 1,000 inhabitants at risk per year.
• Incidence of Tuberculosis: Estimated cases of tuberculosis per 100,000 inhabitants.
  • Includes Low/High CI columns.
• Hepatitis B Surface Antigen: Prevalence of hepatitis B surface antigen.
  • Includes Low/High CI columns.
• Road Traffic Deaths: Number of deaths due to traffic accidents per 100,000 people.
• Poisoning Mortality Rate: Deaths attributed to unintentional poisoning per 100,000 people.
• Conflict and Terrorism Deaths: Number of deaths due to armed conflicts and terrorism.
• Battle Related Deaths: Number of deaths related to battles in an armed conflict.
• % Injury Deaths: Percentage of deaths caused by injuries.
• Suicides Rate: Number of deliberate deaths per 100,000 inhabitants.
• Homicide Rate: Number of homicides per 100,000 inhabitants.

Air Pollution Mortality

• Air Pollution Death Rate Total: Probability of dying fr...

Objective: This study examined cumulative excess mortality in European countries in the year of the Covid-19 pandemic and characterized the dynamics of the pandemic in different countries, focusing on Hungary and the Central and Eastern European region.Methods: Age-standardized cumulative excess mortality was calculated based on weekly mortality data from the EUROSTAT database, and was compared between 2020 and the 2016–2019 reference period in European countries.Results: Cumulate weekly excess mortality in Hungary was in the negative range until week 44. By week 52, it reached 9,998 excess deaths, corresponding to 7.73% cumulative excess mortality vs. 2016–2019 (p-value = 0.030 vs. 2016–2019). In Q1, only Spain and Italy reported excess mortality compared to the reference period. Significant increases in excess mortality were detected between weeks 13 and 26 in Spain, United Kingdom, Belgium, Netherland and Sweden. Romania and Portugal showed the largest increases in age-standardized cumulative excess mortality in the Q3. The majority of Central and Eastern European countries experienced an outstandingly high impact of the pandemic in Q4 in terms of excess deaths. Hungary ranked 11th in cumulative excess mortality based on the latest available data of from the EUROSTAT database.Conclusion: Hungary experienced a mortality deficit in the first half of 2020 compared to previous years, which was followed by an increase in mortality during the second wave of the COVID-19 pandemic, reaching 7.7% cumulative excess mortality by the end of 2020. The excess was lower than in neighboring countries with similar dynamics of the pandemic.

The dataset contains World Bank Suicide mortality rate WDI (world development indicator) (2000-2019) world-wide data in original and processed form. In addition to the statistical data this dataset also contains bibliographic records of articles published on the topic of suicide in relation to individual countries during (2000-2019) in original and processed form.

The data consists of six archives:

World development indicator suicide mortality rate SH.STA.SUIC.P5. This archive contains suicide mortality rate of 159 countries during the period of 2000-2019 per 100,000 population including males and females as of November, 2023.

Web of science records country and suicide. This archive contains bibliographic records organized by country on the topic of suicide related to that country published during 2000-2019 as of November, 2023.

Suicide mortality rate statistics and keywords. This archive contains processed data of 1 and 2 archives in three files. The 'Countries suicide rates and WOS records' contains organized temporal suicide mortality rate data for each country and each year for males and females including counts of articles on suicide related in that country. The 'words and countries matrix' file contains information about how many times author and paper keywords from suicide related publications were seen in articles associated with each country. This data is organized as matrix in which rows are keywords, columns are countries and cells are counts of the keyword. The 'words and countries pairs' file contains same information only organized as keyword country pairs.

Suicide mortality rate clusters countries keywords titles. This archive contains bibliographic data organized by country clusters. These clusters group countries with similar suicide mortality rate dynamics in males and females shown in two included figures. Each folder of the cluster contains a section with bibliographic records; a section with keywords associated with each country; and a section in which each publication associated with the country has a separate filecontaining its title and keywords.

Suicide keywords embedding data. This archive contains word embedding vectors and metadata learned by recurrent neural network trained to classify countries from suicide related keywords of articles associated with those countries. Folder 'trained with keywords' contains embeddings learned in classifying countries in which training samples are keyword strings of publications. Folder 'trained with titles' contains embeddings learned in classifying countries in which training samples are strings containing titles of publication plus keywords.

Suicide keywords association rule mining. This archive contains files of subsets of keywords frequently mentioned together in suicide related publications. Folder 'Mining in clusters' has frequent keyword itemsets in country clusters. Folder 'Mining in individual countries' has frequent keyword itemsets in countries. Examples of keyword networks connecting clusters and networks connecting countries in individual clusters are included which helps to identify specific and shared keywords by country clusters and by countries in the individual clusters.

These datasets support a data availability statements for upcoming articles.

In April 2020 Eurostat set up an exceptional data collection on total weekly deaths, in order to support the policy and research efforts related to Covid-19. With this data collection, Eurostat's target was to provide quickly statistics that show the changing situation of the total number of weekly deaths from early 2020 onwards.

The available data on the total weekly deaths are transmitted by the National Statistical Institutes to Eurostat on voluntary basis. Data are collected cross classified by sex, 5-year age-groups and NUTS3 region (NUTS2021). The age breakdown by 5-year age group is the most significant and should be considered by the reporting countries as the main option; when that is not possible, data may be provided with less granularity. Similar with the regional structure, data granularity varies with the country.

Eurostat requested from the National Statistical Institutes the transmission of a back time series of weekly deaths for as many year as possible, recommending as starting point the year 2000. Shorter time series, imposed by data availability, are transmitted by some countries. A long enough time series is necessary for temporal comparisons and statistical modelling.

A note on Ireland: Data from Ireland were not included in the first phase of the weekly deaths data collection: official timely data were not available because deaths can be registered up to three months after the date of death. Because of the COVID-19 pandemic, the Central Statistics Office of Ireland began to explore experimental ways of obtaining up-to-date mortality data, finding a strong correlation between death notices published on RIP.ie and official mortality statistics. Recently, CSO Ireland started publishing a time series covering the period from October 2019 until the most recent weeks, using death notices (see CSO website). For the purpose of this release, Eurostat compared the new 2020-2021 web-scraped series with a 2016-2019 baseline established using official data. CSO is periodically assessing the quality of these data.

The purpose of Eurostat’s online tables in the folder Weekly deaths - special data collection (demomwk) is to make available to users information on the weekly number of deaths disaggregated by sex, 5 years age group and NUTS3 regions over the last 20 years, depending on the availability in each country covered in Eurostat demographic statistics data collections. In order to ensure the highest timeliness possible, data are made available as reported by the countries, and work is ongoing in order to improve data quality and user friendliness.

Starting in 2025, the weekly deaths data is collected on a quarterly basis. The database updates are expected by mid-June (release of monthly data for 1st quarter of the year), mid-September (2nd quarter), mid-December (3rd quarter), and mid-February (4th quarter).

Context

The 2019–20 coronavirus pandemic is an ongoing pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Source: https://en.wikipedia.org/wiki/2019%E2%80%9320_coronavirus_pandemic.

Content

Coronavirus COVID-19 confirmed cases, deaths, case mortality ratios, country, latitude, and longitude.

Disclaimer: Data will be more accurate as more data comes in. Deaths/Infections will be a better measure of mortality rate after a pandemic is over, when the estimates of the number of infections start to get closer to the true number of infected individuals. Note discussion of case mortality ratio (numbers as they are reported) vs infection mortality ratio (estimates of the actual numbers). This dataset discusses case mortality ratios.

Acknowledgements

Banner photo by Adhy Savala on Unsplash.

Data generated from the notebook https://www.kaggle.com/paultimothymooney/does-latitude-impact-the-spread-of-covid-19 using data from https://www.kaggle.com/paultimothymooney/latitude-and-longitude-for-every-country-and-state and https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset, all of which were released under open data licenses.

Vital Statistics: Death Rate: per 1000 Population: Maharashtra: Rural data was reported at 6.200 NA in 2020. This stayed constant from the previous number of 6.200 NA for 2019. Vital Statistics: Death Rate: per 1000 Population: Maharashtra: Rural data is updated yearly, averaging 7.300 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 8.900 NA in 1998 and a record low of 6.200 NA in 2020. Vital Statistics: Death Rate: per 1000 Population: Maharashtra: Rural data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH003: Vital Statistics: Death Rate: by States.

Germany DE: Number of Deaths Ages 15-19 Years data was reported at 923.000 Person in 2019. This records a decrease from the previous number of 942.000 Person for 2018. Germany DE: Number of Deaths Ages 15-19 Years data is updated yearly, averaging 1,632.000 Person from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 2,544.000 Person in 1990 and a record low of 923.000 Person in 2019. Germany DE: Number of Deaths Ages 15-19 Years data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Health Statistics. Number of deaths of adolescents ages 15-19 years; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Sum; 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.

Vital Statistics: Death Rate: per 1000 Population: Maharashtra data was reported at 5.500 NA in 2020. This records an increase from the previous number of 5.400 NA for 2019. Vital Statistics: Death Rate: per 1000 Population: Maharashtra data is updated yearly, averaging 6.500 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 7.700 NA in 1998 and a record low of 5.400 NA in 2019. Vital Statistics: Death Rate: per 1000 Population: Maharashtra data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH003: Vital Statistics: Death Rate: by States.

In April 2020 Eurostat set up an exceptional data collection on total weekly deaths, in order to support the policy and research efforts related to Covid-19. With this data collection, Eurostat's target was to provide quickly statistics that show the changing situation of the total number of weekly deaths from early 2020 onwards.

The available data on the total weekly deaths are transmitted by the National Statistical Institutes to Eurostat on voluntary basis. Data are collected cross classified by sex, 5-year age-groups and NUTS3 region (NUTS2021). The age breakdown by 5-year age group is the most significant and should be considered by the reporting countries as the main option; when that is not possible, data may be provided with less granularity. Similar with the regional structure, data granularity varies with the country.

Eurostat requested from the National Statistical Institutes the transmission of a back time series of weekly deaths for as many year as possible, recommending as starting point the year 2000. Shorter time series, imposed by data availability, are transmitted by some countries. A long enough time series is necessary for temporal comparisons and statistical modelling.

A note on Ireland: Data from Ireland were not included in the first phase of the weekly deaths data collection: official timely data were not available because deaths can be registered up to three months after the date of death. Because of the COVID-19 pandemic, the Central Statistics Office of Ireland began to explore experimental ways of obtaining up-to-date mortality data, finding a strong correlation between death notices published on RIP.ie and official mortality statistics. Recently, CSO Ireland started publishing a time series covering the period from October 2019 until the most recent weeks, using death notices (see CSO website). For the purpose of this release, Eurostat compared the new 2020-2021 web-scraped series with a 2016-2019 baseline established using official data. CSO is periodically assessing the quality of these data.

The purpose of Eurostat’s online tables in the folder Weekly deaths - special data collection (demomwk) is to make available to users information on the weekly number of deaths disaggregated by sex, 5 years age group and NUTS3 regions over the last 20 years, depending on the availability in each country covered in Eurostat demographic statistics data collections. In order to ensure the highest timeliness possible, data are made available as reported by the countries, and work is ongoing in order to improve data quality and user friendliness.

Starting in 2025, the weekly deaths data is collected on a quarterly basis. The database updates are expected by mid-June (release of monthly data for 1st quarter of the year), mid-September (2nd quarter), mid-December (3rd quarter), and mid-February (4th quarter).

Vital Statistics: Death Rate: per 1000 Population: West Bengal data was reported at 5.500 NA in 2020. This records an increase from the previous number of 5.300 NA for 2019. Vital Statistics: Death Rate: per 1000 Population: West Bengal data is updated yearly, averaging 6.200 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 7.500 NA in 1998 and a record low of 5.300 NA in 2019. Vital Statistics: Death Rate: per 1000 Population: West Bengal data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH003: Vital Statistics: Death Rate: by States.

The monthly excess mortality indicator is based on the exceptional data collection on weekly deaths that Eurostat and the National Statistical Institutes set up, in April 2020, in order to support the policy and research efforts related to the COVID-19 pandemic. With that data collection, Eurostat's target was to provide quickly statistics assessing the changing situation of the total number of deaths on a weekly basis, from early 2020 onwards.

The National Statistical Institutes transmit available data on total weekly deaths, classified by sex, 5-year age groups and NUTS3 regions (NUTS2021) over the last 20 years, on a voluntary basis. The resulting online tables, and complementary metadata, are available in the folder Weekly deaths - special data collection (demomwk).

Starting in 2025, the weekly deaths data collected on a quarterly basis. The database updated on the 16th of June 2025 (1st quarter), on the 16 th of September 2025 (2nd quarter), and next update will be in mid-December 2025 (3rd quarter), and mid-February 2026 (4th quarter).

In December 2020, Eurostat released the European Recovery Statistical Dashboard containing also indicators tracking economic and social developments, including health. In this context, “excess mortality” offers elements for monitoring and further analysing direct and indirect effects of the COVID-19 pandemic.

The monthly excess mortality indicator draws attention to the magnitude of the crisis by providing a comprehensive comparison of additional deaths amongst the European countries and allowing for further analysis of its causes. The number of deaths from all causes is compared with the expected number of deaths during a certain period in the past (baseline period, 2016-2019).

The reasons that excess mortality may vary according to different phenomena are that the indicator is comparing the total number of deaths from all causes with the expected number of deaths during a certain period in the past (baseline). While a substantial increase largely coincides with a COVID-19 outbreak in each country, the indicator does not make a distinction between causes of death. Similarly, it does not take into account changes over time and differences between countries in terms of the size and age/sex structure of the population Statistics on excess deaths provide information about the burden of mortality potentially related to the COVID-19 pandemic, thereby covering not only deaths that are directly attributed to the virus but also those indirectly related to or even due to another reason. For example, In July 2022, several countries recorded unusually high numbers of excess deaths compared to the same month of 2020 and 2021, a situation probably connected not only to COVID-19 but also to the heatwaves that affected parts of Europe during the reference period.

In addition to confirmed deaths, excess mortality captures COVID-19 deaths that were not correctly diagnosed and reported, as well as deaths from other causes that may be attributed to the overall crisis. It also accounts for the partial absence of deaths from other causes like accidents that did not occur due, for example, to the limitations in commuting or travel during the lockdown periods.

Vital Statistics: Death Rate: per 1000 Population: Uttar Pradesh: Urban data was reported at 5.400 NA in 2020. This records an increase from the previous number of 5.300 NA for 2019. Vital Statistics: Death Rate: per 1000 Population: Uttar Pradesh: Urban data is updated yearly, averaging 6.300 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 8.100 NA in 1999 and a record low of 5.300 NA in 2019. Vital Statistics: Death Rate: per 1000 Population: Uttar Pradesh: Urban data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH003: Vital Statistics: Death Rate: by States.