The highest number of deaths due to falls in the United States was 46,653 in 2022. This statistic shows a timeline of the number of unintentional-injury-related deaths due to falls in the United States from 1905 to 2022.

This statistic shows the number of deaths as a result of a fall in Scotland in 2018, by age. Of the 940 people who died because of a fall in 2018, 794 were over the age of 75.

This statistic shows the death rate from falls among adults aged 65 years and older in the U.S. in 2007 and 2016, by age. In 2007, the rate of death among adults aged between 65 to 74 years due to falls was 13.2 per 100,000. This rate increased to 15.6 per 100,000 in the year 2016.

Decrease the rate of unintentional fall-related injury fatalities of older adults from 80.1 per 100,000 in 2013 to 68.1 per 100,000 by 2017.

In 2021, approximately 25.7 thousand people in Italy died from external causes of injury and poisoning. In that year, accidental falls were the accident causing the highest number of deaths, with 4,947 in total. Moreover, 3,020 male individuals died of suicide or intentional self-harm. This statistic depicts the total number of deaths from external causes of injury and poisoning in Italy in 2021, by gender

Effective September 27, 2023, this dataset will no longer be updated. Similar data are accessible from wonder.cdc.gov. Deaths involving COVID-19, influenza, and pneumonia reported to NCHS by jurisdiction of occurrence, place of death, and age group.

In 2020, there were around 3,356 deaths among adults aged 65 years and over in Florida due to unintentional falls. This statistic displays the number of deaths due to unintentional falls for adults aged 65 and over in the United States in 2020, by state.

Provisional counts of deaths by the month the deaths occurred, by age group and race/ethnicity, for select underlying causes of death for 2020-2021. Final data is provided for 2019. The dataset also includes monthly provisional counts of death for COVID-19, coded to ICD-10 code U07.1 as an underlying or multiple cause of death.

Almost 3.4 thousand people died due to accidents in Sweden in 2023. The most common accident that led to death was falling accidents, reaching over 1.1 thousand. Furthermore, 421 deaths were due to accidental poisoning that year.

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, hospitalizations, and associated deaths that have been reported among Connecticut residents. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Hospitalization data were collected by the Connecticut Hospital Association and reflect the number of patients currently hospitalized with laboratory-confirmed COVID-19. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the daily COVID-19 update. 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 reported d

The Detailed Mortality - Underlying Cause of Death data on CDC WONDER are county-level national mortality and population data spanning the years 1999-2009. Data are based on death certificates for U.S. residents. Each death certificate contains a single underlying cause of death, and demographic data. The number of deaths, crude death rates, age-adjusted death rates, standard errors and 95% confidence intervals for death rates can be obtained by place of residence (total U.S., region, state, and county), age group (including infants and single-year-of-age cohorts), race (4 groups), Hispanic ethnicity, gender, year of death, and cause-of-death (4-digit ICD-10 code or group of codes, injury intent and mechanism categories, or drug and alcohol related causes), year, month and week day of death, place of death and whether an autopsy was performed. The data are produced by the National Center for Health Statistics.

The National Violent Death Reporting System (NVDRS) provides states and communities with a clearer understanding of violent deaths to guide local decisions about efforts to prevent violence and helps them track progress over time.

To stop violent deaths, we must first understand all the facts. Created in 2002, the NVDRS is a surveillance system that pulls together data on violent deaths in 18 states (see map below), including information about homicides, such as homicides perpetrated by a intimate partner (e.g., boyfriend, girlfriend, wife, husband), child maltreatment (or child abuse) fatalities, suicides, deaths where individuals are killed by law enforcement in the line of duty, unintentional firearm injury deaths, and deaths of undetermined intent.

These data are supported by WISQARS, an interactive query system that provides data on injury deaths, violent deaths, and nonfatal injuries.

Series Name: Neonatal mortality rate (deaths per 1 000 live births)Series Code: SH_DYN_NMRTRelease Version: 2020.Q2.G.03This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 3.2.2: Neonatal mortality rateTarget 3.2: By 2030, end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live birthsGoal 3: Ensure healthy lives and promote well-being for all at all agesFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Numbers of deaths registered by cause.

Note: Starting April 27, 2023 updates change from daily to weekly. Summary The cumulative number of probable COVID-19 deaths among Maryland residents by race and ethnicity: African American; White; Hispanic; Asian; Other; Unknown. Description The MD COVID-19 - Probable Deaths by Race and Ethnicity Distribution data layer is a collection of the statewide confirmed and probable COVID-19 related deaths that have been reported each day by the Vital Statistics Administration by categories of race and ethnicity. A death is classified as probable if the person's death certificate notes COVID-19 to be a probable, suspect or presumed cause or condition. Probable deaths are not yet been confirmed by a laboratory test. Some data on deaths may be unavailable due to the time lag between the death, typically reported by a hospital or other facility, and the submission of the complete death certificate. Confirmed deaths are available from the MD COVID-19 - Confirmed Deaths by Race and Ethnicity Distribution data layer. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

France Deaths: Women: Other Causes data was reported at 74,539.000 Person in 2015. This records an increase from the previous number of 67,918.000 Person for 2014. France Deaths: Women: Other Causes data is updated yearly, averaging 56,599.000 Person from Dec 1996 (Median) to 2015, with 20 observations. The data reached an all-time high of 74,539.000 Person in 2015 and a record low of 44,028.000 Person in 1996. France Deaths: Women: Other Causes data remains active status in CEIC and is reported by French National Institute for Statistics and Economic Studies. The data is categorized under Global Database’s France – Table FR.G062: Health Statistics: Causes of Death.

Deaths aged 5 to 24

No. of Deaths: Caused by: Enteritis, Non-Infectious Colitis & Other Diseases of the Intestines data was reported at 495.000 Person in Sep 2024. This records an increase from the previous number of 475.000 Person for Jun 2024. No. of Deaths: Caused by: Enteritis, Non-Infectious Colitis & Other Diseases of the Intestines data is updated quarterly, averaging 381.500 Person from Mar 2017 (Median) to Sep 2024, with 30 observations. The data reached an all-time high of 500.000 Person in Dec 2023 and a record low of 276.000 Person in Jun 2020. No. of Deaths: Caused by: Enteritis, Non-Infectious Colitis & Other Diseases of the Intestines data remains active status in CEIC and is reported by National Administrative Department of Statistics. The data is categorized under Global Database’s Colombia – Table CO.G012: Number of Deaths: Cause of Death.

Series Name: Maternal mortality ratioSeries Code: SH_STA_MMRRelease Version: 2020.Q2.G.03This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 3.1.1: Maternal mortality ratioTarget 3.1: By 2030, reduce the global maternal mortality ratio to less than 70 per 100,000 live birthsGoal 3: Ensure healthy lives and promote well-being for all at all agesFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Results data for the thesis on estimating the age-, sex-, cause-specific excess mortality during the COVID-19 pandemic in Hong Kong and South Korea.Thesis abstractBackgroundFew studies used a consistent methodology and adjusted for the risk of influenza-like illness (ILI) in historical mortality trends when estimating and comparing the cause-specific excess mortality (EM) during the COVID-19 pandemic. Previous studies demonstrated that excess mortality was widely reported from CVD and among the elderly. This study aims to estimate and compare the overall, age-, sex-, and cause-specific excess mortality during the COVID-19 pandemic in Hong Kong (HK) and South Korea (SK) with consideration of the impact of ILI.MethodsIn this population-based study, we first fitted a generalized additive model to the monthly mortality data from Jan 2010 to Dec 2019 in HK and SK before the COVID-19 pandemic. Then we applied the fitted model to estimate the EM from Jan 2020 to Dec 2022. The month index was modelled with a natural cubic spline. Akaike information criterion (AIC) was used to select the number of knots for the spline and inclusion of covariates such as monthly mean temperature, absolute humidity, ILI consultation rate, and the proxy for flu activity.FindingsFrom 2020 to 2022, the EM in HK was 239.8 (95% CrI: 184.6 to 293.9) per 100,000 population. Excess mortality from respiratory diseases (RD) (ICD-10 code: J00-J99), including COVID-19 deaths coded as J98.8, was 181.3 (95% CrI: 149.9 to 210.4) per 100,000. Except for RD, the majority of the EM in HK was estimated from cardiovascular diseases (CVD) (22.4% of the overall EM), influenza and pneumonia (16.2%), ischemic heart disease (8.9%), ill-defined causes (8.6%) and senility (6.7%). No statistically significant reduced deaths were estimated among other studied causes.From 2020 to 2022, the EM in SK was 204.7 (95% CrI: 161.6 to 247.2) per 100,000 population. Of note, COVID-19 deaths in SK were not included in deaths from RD but were recorded with the codes for emergency use as U07.1 or U07.2. The majority of the EM was estimated from ill-defined causes (32.0% of the overall EM), senility (16.6%), cerebrovascular disease (6.8%) and cardiovascular diseases (6.1%). Statistically significant reduction in mortality with 95 CrI lower than zero was estimated from vascular, other and unspecified dementia (-26.9% of expected deaths), influenza and pneumonia (-20.7%), mental and behavioural disorders (-18.8%) and respiratory diseases (-7.7%).InterpretationExcluding RD in HK which includes COVID-19 deaths, the majority of the EM in HK and SK was from CVD and senility. Mortality from influenza and pneumonia was estimated to have a statistically significant increase in HK but a decrease in SK probability due to different coding practices. HK had a heavier burden of excess mortality in the elderly age group 70-79 years and 80 years or above, while SK had a heavier burden in the age group of 60-69 years. Both HK and SK have a heavier burden of excess mortality from males than females. Better triage systems for identifying high-risk people of the direct or indirect impact of the epidemic are needed to minimize preventable mortality.