Multnomah county was the most affected county in Oregon during the heatwave in 2021. Multnomah reported 72 suspected heat-related deaths as of July 7, 2021. Of these, 46 were formally declared hyperthermia. Marion county followed with the second highest figures, with 13 heat-related deaths. Oregon was among the states most affected by the heatwave in the Pacific Northwest from late June to mid-July 2021, with temperatures breaking daily record highs. Most of the heatwave-related deaths were of adults within the age group of 60 to 69 year-olds.

Since 1900, the earthquake in Tangshan in China in 1976 caused the highest number of deaths, reaching over 240,000. However, some estimate the number to be over 650,000 fatalities. The earthquake in Haiti in 2010 has the second-highest death toll, but also here numbers vary from just above 100,000 to over 300,000 fatalities. Four of the 10 deadliest earthquakes during the period were registered in China.

This dataset reports the daily reported number of deaths involving COVID-19 by fatality type. Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak. Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool Data includes: * Date on which the death occurred * Total number of deaths involving COVID-19 * Number of deaths with “COVID-19 as the underlying cause of death” * Number of deaths with “COVID-19 contributed but not underlying cause” * Number of deaths where the “Cause of death unknown” or “Cause of death missing” ##Additional Notes The method used to count COVID-19 deaths has changed, effective December 1, 2022. Prior to December 1 2022, deaths were counted based on the date the death was updated in the public health unit’s system. Going forward, deaths are counted on the date they occurred. On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023. CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags. As of December 1, 2022, data are based on the date on which the death occurred. This reporting method differs from the prior method which is based on net change in COVID-19 deaths reported day over day. Data are based on net change in COVID-19 deaths for which COVID-19 caused the death reported day over day. Deaths are not reported by the date on which death happened as reporting may include deaths that happened on previous dates. Spikes, negative numbers and other data anomalies: Due to ongoing data entry and data quality assurance activities in Case and Contact Management system (CCM) file, Public Health Units continually clean up COVID-19, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes, negative numbers and current totals being different from previously reported case and death counts. Public Health Units report cause of death in the CCM based on information available to them at the time of reporting and in accordance with definitions provided by Public Health Ontario. The medical certificate of death is the official record and the cause of death could be different. Deaths are defined per the outcome field in CCM marked as “Fatal”. Deaths in COVID-19 cases identified as unrelated to COVID-19 are not included in the number of deaths involving COVID-19 reported. "_Cause of death unknown_" is the category of death for COVID-19 positive individuals with cause of death still under investigation, or for which the public health unit was unable to determine cause of death. The category may change later when the cause of death is confirmed either as “COVID-19 as the underlying cause of death”, “COVID-19 contributed but not underlying cause,” or “COVID-19 unrelated”. "_Cause of death missing_" is the category of death for COVID-19 positive individuals with the cause of death missing in CCM. Rates for the most recent days are subject to reporting lags All data reflects totals from 8 p.m. the previous day. This dataset is subject to change.

This statistic shows the ten countries with the most deaths resulting from earthquakes between 1900 and 2016. Total 876,487 people were killed due to earthquakes in China. Fatalities around the world The leading causes of death worldwide for humans in 2012 were ischaemic heart diseases, with 7.4 million deaths and strokes, with 6.7 million deaths. Apart from these diseases, there are many other dangers for humans all over the world, such as famine, drugs, epidemics or the everyday traffic.

The global famine death rate has decreased over the past decades, 814 people per 100,000 of the global population died as a result of famine, while the number of deaths due to famine was about 3 per 100,000 of the global population in 2000. Famine is a scarcity of food, which can be caused by crop failure, population unbalance or drought. Between 1900 and 2014, the number of deaths due to droughts stood at 3,000,000 in China.

Compared to other countries, the Unites States are ranked as the country with the highest amount of drug-related deaths around the world. 40,393 people passed away due to drugs in 2012, while only 944 drug-related deaths were reported in Germany.

The Ebola outbreak in West Africa is one of the largest outbreaks in history and costs the life of many people. The Ebola virus disease has a high risk of deaths, as of August 26, 2014 there have been 3,069 cases, resulting 1,552 deaths due to outbreak in West Africa.

According to the World Health Organization (WHO), 162 annual traffic fatalities per 100,000 registered vehicles were counted in South Africa, which is the country with the highest number of road-traffic fatalities from 2006 – 2008. Germany is on of the country with the lowest annual traffic fatalities, there were only 9 traffic fatalities per 100,000 registered vehicles.

This mapping tool enables you to see how COVID-19 deaths in your area may relate to factors in the local population, which research has shown are associated with COVID-19 mortality. It maps COVID-19 deaths rates for small areas of London (known as MSOAs) and enables you to compare these to a number of other factors including the Index of Multiple Deprivation, the age and ethnicity of the local population, extent of pre-existing health conditions in the local population, and occupational data. Research has shown that the mortality risk from COVID-19 is higher for people of older age groups, for men, for people with pre-existing health conditions, and for people from BAME backgrounds. London boroughs had some of the highest mortality rates from COVID-19 based on data to April 17th 2020, based on data from the Office for National Statistics (ONS). Analysis from the ONS has also shown how mortality is also related to socio-economic issues such as occupations classified ‘at risk’ and area deprivation. There is much about COVID-19-related mortality that is still not fully understood, including the intersection between the different factors e.g. relationship between BAME groups and occupation. On their own, none of these individual factors correlate strongly with deaths for these small areas. This is most likely because the most relevant factors will vary from area to area. In some cases it may relate to the age of the population, in others it may relate to the prevalence of underlying health conditions, area deprivation or the proportion of the population working in ‘at risk occupations’, and in some cases a combination of these or none of them. Further descriptive analysis of the factors in this tool can be found here: https://data.london.gov.uk/dataset/covid-19--socio-economic-risk-factors-briefing

This mapping tool enables you to see how COVID-19 deaths in your area may relate to factors in the local population, which research has shown are associated with COVID-19 mortality. It maps COVID-19 deaths rates for small areas of London (known as MSOAs) and enables you to compare these to a number of other factors including the Index of Multiple Deprivation, the age and ethnicity of the local population, extent of pre-existing health conditions in the local population, and occupational data. Research has shown that the mortality risk from COVID-19 is higher for people of older age groups, for men, for people with pre-existing health conditions, and for people from BAME backgrounds. London boroughs had some of the highest mortality rates from COVID-19 based on data to April 17th 2020, based on data from the Office for National Statistics (ONS). Analysis from the ONS has also shown how mortality is also related to socio-economic issues such as occupations classified ‘at risk’ and area deprivation. There is much about COVID-19-related mortality that is still not fully understood, including the intersection between the different factors e.g. relationship between BAME groups and occupation. On their own, none of these individual factors correlate strongly with deaths for these small areas. This is most likely because the most relevant factors will vary from area to area. In some cases it may relate to the age of the population, in others it may relate to the prevalence of underlying health conditions, area deprivation or the proportion of the population working in ‘at risk occupations’, and in some cases a combination of these or none of them. Further descriptive analysis of the factors in this tool can be found here: https://data.london.gov.uk/dataset/covid-19--socio-economic-risk-factors-briefing

Find data on deaths of Massachusetts residents. Information is obtained from death certificates received by the Registry of Vital Records and Statistics.

Visualize leading causes of death in the United States for ages 1-44 from 1981 to present. 10 Leading Causes of Death, United States 2022, All Deaths with drilldown to ICD codes, All Sexes, All Races, All Ethnicities

The World Health Organization reported 6932591 Coronavirus Deaths since the epidemic began. In addition, countries reported 766440796 Coronavirus Cases. This dataset provides - World Coronavirus Deaths- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset reports the daily reported number of the 7-day moving average rates of Deaths involving COVID-19 by vaccination status and by age group. Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak. Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool Data includes: * Date on which the death occurred * Age group * 7-day moving average of the last seven days of the death rate per 100,000 for those not fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those vaccinated with at least one booster ##Additional notes As of June 16, all COVID-19 datasets will be updated weekly on Thursdays by 2pm. As of January 12, 2024, data from the date of January 1, 2024 onwards reflect updated population estimates. This update specifically impacts data for the 'not fully vaccinated' category. On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023. CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags. The data does not include vaccination data for people who did not provide consent for vaccination records to be entered into the provincial COVaxON system. This includes individual records as well as records from some Indigenous communities where those communities have not consented to including vaccination information in COVaxON. “Not fully vaccinated” category includes people with no vaccine and one dose of double-dose vaccine. “People with one dose of double-dose vaccine” category has a small and constantly changing number. The combination will stabilize the results. Spikes, negative numbers and other data anomalies: Due to ongoing data entry and data quality assurance activities in Case and Contact Management system (CCM) file, Public Health Units continually clean up COVID-19, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes, negative numbers and current totals being different from previously reported case and death counts. Public Health Units report cause of death in the CCM based on information available to them at the time of reporting and in accordance with definitions provided by Public Health Ontario. The medical certificate of death is the official record and the cause of death could be different. Deaths are defined per the outcome field in CCM marked as “Fatal”. Deaths in COVID-19 cases identified as unrelated to COVID-19 are not included in the Deaths involving COVID-19 reported. Rates for the most recent days are subject to reporting lags All data reflects totals from 8 p.m. the previous day. This dataset is subject to change.

By December 2017, the official death toll in Puerto Rico due to Hurricane Maria was set at 64 excess deaths. To verify the validity of this death count, a study by Kishore et al used a community-based survey sampling method to compute an empirical measurement of the death count. The study sampled from approximately 3000 households, then compared the estimated deaths with the vital statistics data from 2016 through the end of December 2017. The study method estimated 4645 excess deaths with a 95% confidence interval from 793 to 8498 potential excess deaths. These estimated excess mortality shows a markedly high estimate with a wide confidence interval, but despite these issues the estimates do indicate that the official death tool is a significantly underestimate of the realistic excess deaths in the population.

Due to copy-right permissions, the article should be accessed at the source website. Please use the following reference citation and doi to redirect there: Kishore N, Marqués D, Mahmud A, Kiang MV, Rodriguez I, Fuller A, Ebner P, Sorensen C, Racy F, Lemery J, Maas L. Mortality in Puerto Rico after Hurricane Maria. New England journal of medicine. 2018 Jul 12;379(2):162-70. http://dx.doi.org/10.1056/NEJMsa1803972

IntroductionIt is challenging to prognosticate hospitalised older adults. Delayed recognition of end-of-life leads to failure in delivering appropriate palliative care and increases healthcare utilisation. Most mortality prediction tools specific for older adults require additional manual input, resulting in poor uptake. By leveraging on electronic health records, we aim to create an automatable mortality prediction tool for hospitalised older adults.MethodsWe retrospectively reviewed electronic records of general medicine patients ≥75 years at a tertiary hospital between April–September 2021. Demographics, comorbidities, ICD-codes, age-adjusted Charlson Comorbidity Index (CCI), Hospital Frailty Risk Score, mortality and resource utilization were collected. We defined early deaths, late deaths and survivors as patients who died within 30 days, 1 year, and lived beyond 1 year of admission, respectively. Multivariate logistic regression analyses were adjusted for age, gender, race, frailty, and CCI. The final prediction model was created using a stepwise logistic regression.ResultsOf 1,224 patients, 168 (13.7%) died early and 370 (30.2%) died late. From adjusted multivariate regression, risk of early death was significantly associated with ≥85 years, intermediate or high frail risk, CCI > 6, cardiovascular risk factors, AMI and pneumonia. For late death, risk factors included ≥85 years, intermediate frail risk, CCI >6, delirium, diabetes, AMI and pneumonia. Our mortality prediction tool which scores 1 point each for age, pneumonia and AMI had an AUC of 0.752 for early death and 0.691 for late death.ConclusionOur mortality prediction model is a proof-of-concept demonstrating the potential for automated medical alerts to guide physicians towards personalised care for hospitalised older adults.

1. Number of deaths in New Taipei City - by age group (including gender)2. Unit: person; years old3. For detailed explanations of each field, please refer to the electronic file of gender statistics for New Taipei City (website: http://www.bas.ntpc.gov.tw/home.jsp?idMTI5) or contact the Department of Budget, Accounting and Statistics for inquiries.

In high, medium, and low estimations, the number of male and female deaths and the crude death rate for around 50 years in the future.

This statistic displays a timeline of the annual number of deaths in Mexico in 2015, as well as a forecast until 2050. It is estimated that between 2015 and 2050, the death toll in Mexico will increase by more than 630 thousands deaths.To find out about the annual number of births in Mexico in 2015, as well as a forecast until 2050, please click here.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This was a two phase project designed to investigate the decision-making process of the coroner/medical examiner (CME) offices who are charged with investigating suspicious elder deaths and to pilot an intervention that augmented the decision-making process in three CME offices. In phase one, researchers collected case data from CME offices, public data on elder deaths, and interviews with CME investigators. Researchers then developed a brief screening tool, Elder Suspicious Death Field Screen (ESDFS), to be used by CME employees fielding reports of elder deaths. In phase two, the ESDFS was implemented in three counties for a six-month data collection period. An expert panel reviewed a subsample of cases to assess whether CME investigators made appropriate decisions to investigate or not.

The Small Arms Survey tracks statistics on violent deaths and compiles them in its database on violent deaths. Within the framework of the 2030 Agenda for Sustainable Development and its Sustainable Development Goals (SDGs), states have pledged to ‘[p]romote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels’. The first target identified under this goal, Target 16.1, commits all states to ‘[s]ignificantly reduce all forms of violence and related death rates everywhere'. The database provides a tool for assessing progress in implementing Target 16.1.

The database contains data from 2004 and includes direct conflict deaths and homicide data sets as well data on 'unintentional homicides' and 'legal interventions deaths'. The database served as the backbone of the Global Burden of Armed Violence reports. Data will be updated and shared once a year.

Abstract INTRODUCTION: In March 2020, the World Health Organization declared the coronavirus disease (COVID-19) outbreak a pandemic. In Brazil, 110 thousand cases and 5,901 deaths were confirmed by the end of April 2020. The scarcity of laboratory resources, the overload on the service network, and the broad clinical spectrum of the disease make it difficult to document all the deaths due to COVID-19. The aim of this study was to assess the mortality rate in Brazilian capitals with a high incidence of COVID-19. METHODS: We assessed the weekly mortality between epidemiological week 1 and 16 in 2020 and the corresponding period in 2019. We estimated the expected mortality at 95% confidence interval by projecting the mortality in 2019 to the population in 2020, using data from the National Association of Civil Registrars (ARPEN-Brasil). RESULTS: In the five capitals with the highest incidence of COVID-19, we identified excess deaths during the pandemic. The age group above 60 years was severely affected, while 31% of the excess deaths occurred in the age group of 20-59 years. There was a strong correlation (r = 0.94) between excess deaths and the number of deaths confirmed by epidemiological monitoring. The epidemiological surveillance captured only 52% of all mortality associated with the COVID-19 pandemic in the cities examined. CONCLUSIONS: Considering the simplicity of the method and its low cost, we believe that the assessment of excess mortality associated with the COVID-19 pandemic should be used as a complementary tool for regular epidemiological surveillance.

The number of deaths of people aged 15 and above in various districts of Taichung City, by gender, age, and marital status of the deceased (by date of occurrence)