Number of deaths and age-specific mortality rates for selected grouped causes, by age group and sex, 2000 to most recent year.

For the week ending June 27, 2025, weekly deaths in England and Wales were 568 below the number expected, compared with 375 below what was expected in the previous week. In late 2022 and through early 2023, excess deaths were elevated for a number of weeks, with the excess deaths figure for the week ending January 13, 2023, the highest since February 2021. In the middle of April 2020, at the height of the COVID-19 pandemic, there were almost 12,000 excess deaths a week recorded in England and Wales. It was not until two months later, in the week ending June 19, 2020, that the number of deaths began to be lower than the five-year average for the corresponding week. Most deaths since 1918 in 2020 In 2020, there were 689,629 deaths in the United Kingdom, making that year the deadliest since 1918, at the height of the Spanish influenza pandemic. As seen in the excess death figures, April 2020 was by far the worst month in terms of deaths during the pandemic. The weekly number of deaths for weeks 16 and 17 of that year were 22,351, and 21,997 respectively. Although the number of deaths fell to more usual levels for the rest of that year, a winter wave of the disease led to a high number of deaths in January 2021, with 18,676 deaths recorded in the fourth week of that year. For the whole of 2021, there were 667,479 deaths in the UK, 22,150 fewer than in 2020. Life expectancy in the UK goes into reverse In 2022, life expectancy at birth for women in the UK was 82.6 years, while for men it was 78.6 years. This was the lowest life expectancy in the country for ten years, and came after life expectancy improvements stalled throughout the 2010s, and then declined from 2020 onwards. There is also quite a significant regional difference in life expectancy in the UK. In the London borough of Kensington and Chelsea, for example, the life expectancy for men was 81.5 years, and 86.5 years for women. By contrast, in Blackpool, in North West England, male life expectancy was just 73.1 years, while for women, life expectancy was lowest in Glasgow, at 78 years.

The attached dataset comprises 187 records, summarized by 2010 census tract. There are 40 variable fields including percent landcover type from the 2011 30m National Land Cover Dataset, density of greenway trails from Wake County (NC) gov't, and demographic attributes from the 2014 American Community Survey. Two fields reflect count (during 2013-2015) and rate of sudden death; these fields are blank because these human-health data are protected under IRB agreement through UNC. The EPA/ORD point of contact for this analysis is Dr. Laura Jackson (jackson.laura@epa.gov). If interested in acessing the Wake County sudden death dataset, please contact Dr. Ross Simpson (ross_simpson@med.unc.edu). This dataset is associated with the following publication: Wu, J., K. Rappazzo, R. Simpson, G. Joodi, I. Pursell, P. Mounsey, W. Cascio, and L. Jackson. Exploring links between greenspace and sudden unexpected death: a spatial analysis. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, NETHERLANDS, 113: 114-121, (2018).

This dataset is no longer updated as of April 2023.

Basic Metadata Note: The Sudden Infant Death Syndrome (SIDS) Rate is infant deaths (under one year of age) due to SIDS per 1,000 live births, by geography. Data set includes registered deaths only. Numerator represents infant's race/ethnicity. Denominator represents mother's race/ethnicity.

**Blank Cells: Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.

***API: Asian/Pacific Islander. ***AIAN: American Indian/Alaska Native.

Sources: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System, 2016. Prepared by: County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics Unit, 2019.

Codes: ICD‐10 Mortality code R95.

Data Guide, Dictionary, and Codebook: https://www.sandiegocounty.gov/content/dam/sdc/hhsa/programs/phs/CHS/Community%20Profiles/Public%20Health%20Services%20Codebook_Data%20Guide_Metadata_10.2.19.xlsx

Interpretation: "There were 5 SIDS deaths per 1,000 live births in Geography X".

In 2023, about **** million deaths were reported in the United States. This figure is an increase from **** million deaths reported in 1990, and from **** in 2019. This sudden increase can be attributed to the COVID-19 pandemic.

This dataset is no longer updated as of April 2023.

Basic Metadata Note: The Sudden Infant Death Syndrome (SIDS) Rate is infant deaths (under one year of age) due to SIDS per 1,000 live births, by geography. Data set includes linked births to deaths. Numerator represents infant's race/ethnicity. Denominator represents mother's race/ethnicity.

**Blank Cells: Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.

***API: Asian/Pacific Islander. ***AIAN: American Indian/Alaska Native.

Sources: State of California, Department of Public Health, Death Statistical Master Files (before 2014), California Comprehensive Death Files (2014 and later), and Birth Statistical Master Files. Prepared by: County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics Unit, 2019.

Codes: ICD‐10 Mortality code R95.

Data Guide, Dictionary, and Codebook: https://www.sandiegocounty.gov/content/dam/sdc/hhsa/programs/phs/CHS/Community%20Profiles/Public%20Health%20Services%20Codebook_Data%20Guide_Metadata_10.2.19.xlsx

Interpretation: "There were 5 SIDS deaths per 1,000 live births in Geography X".

In most middle-aged and older adults, sudden cardiac death is caused by coronary artery disease. In contrast, sudden cardiac death in individuals aged less than 35 years is frequently caused by inherited disorders of cardiac muscle (cardiomyopathies) and cardiac rhythm (ion channelopathies). The genetic nature of many of these diseases means that the relatives of young sudden cardiac death victims are at risk of similar events. In 2004, chapter 8 of the Department of Health's National Service Framework for coronary heart disease recommended family assessment when a sudden cardiac death occurs in a young person. In response to this challenge, The UK Cardiac Pathology Network (UK CPN) was formed in order to provide local coroners with an expert cardiac pathology service and to promote best pathological practice in sudden death cases. A national database allowing UK CPN pathologists to record information on cases referred to them was launched in November 2008 in partnership with the Health and Social Care Information Centre (HSCIC) and the Department of Health. This third report describes the data collected up to and including January 2013.

Provisional counts of the number of deaths registered in England and Wales, by age, sex, region and Index of Multiple Deprivation (IMD), in the latest weeks for which data are available.

From 2016 to 2020, the rate of sudden infant death syndrome among Hispanics in the United States was 21.2 per 100,000 live births. This statistic shows the rates of sudden unexpected infant death (SUID) in the U.S. from 2016 to 2020, by cause and race and ethnicity.

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.

This is historical data. The update frequency has been set to "Static Data" and is here for historic value. Updated on 8/14/2024 Sudden Unexpected Infant Death Rate - This indicator shows the rate of sudden unexpected infant deaths (SUIDs) per 1,000 live births. Sudden unexpected infant deaths (SUIDs) include deaths from Sudden Infant Death Syndrome (SIDS), unknown cause, accidental suffocation and strangulation in bed. Three hundred and sixty-two babies died from SUIDs in Maryland from 2005-2009. Link to Data Details

Number of deaths caused by diseases of the circulatory system, by age group and sex, 2000 to most recent year.

Background:

A PIONEER synthetic dataset of 20,000 ethnically diverse hypertrophic cardiomyopathy patients created using CT-GAN generative AI. Data includes clinical & biological phenotyping, co-morbidities, investigations (ECG, ECHO), procedures & outcomes.

Well-created synthetic data establishes a governance risk-free environment for algorithm development & experimentation. This includes evaluating new treatment models, care management systems, clinical decision support, and more. Synthetic data is of particular use in rare diseases, where real data may be in short supply, or to replicate disease in less common patient demographics (e.g. ethnicities).

Familial hypertrophic cardiomyopathy (HCM) is a rare genetic condition characterised by thickening (hypertrophy) of the cardiac muscle, usually of the interventricular septum. Arrhythmias can be life threatening and HCM is associated with an increased risk of sudden death. Some affected individuals develop potentially fatal heart failure, which may require heart transplantation. Approximately 130,000 people have HCM in the UK, but there is a significant burden of undiagnosed disease and diagnostic delay.

Geography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.

Available supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and can provide real world data to meet bespoke requirements.

Available supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.

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.

In 2022, the rate of sudden infant death syndrome in the United States was around ** per 100,000 live births. This was a significant decrease from a rate of *** per 100,000 live births in the year 1990. This statistic shows the rates of sudden unexpected infant death (SUID) in the U.S. from 1990 to 2022, by cause of death.

The data shows the year-wise statistics for incidence of accidental deaths in different cities of India by natural or unnatural causes between 2009 and 2015.

Note: 1. Vasai Virar, Tiruchirappalli, Thrissur, Thiruvananthapuram, Ranchi, Srinagar, Raipur, Malappuram, Kozhikode, Kota, Kollam, Kannur, Jodhpur, Gwalior, Ghaziabad, Durg Bhilainagar, Aurangabad and Chandigarh (City) newly emerged Mega Cities as per Population Census 2011. 2. Poisoning includes the incidence due to food poisoning/accidental intake of insects, spurious/poisoning liquor, leakage of poisoning gases etc., snake bite/animal bite and others. 3. Traffic accidents includes Road accidents, Rail road accidents and other railway accidents. 4. Collapse of structure includes House, Building, Dam, Bridge others. 5. Sudden deaths include i) Heart Attacks ii) Epileptic fits/giddiness iii) Abortion/Childbirth iv) Influence of alcohol. 6. Fire includes i) Fireworks/crackers ii) Short-Circuit iii) Cooking Gas Cylinder/Stove burst iv) other fire accidents.

In 2022, the infant mortality rate in the United States was 5.4 out of every 1,000 live births. This is a significant decrease from 1960, when infant mortality was at around 26 deaths out of every 1,000 live births. What is infant mortality? The infant mortality rate is the number of deaths of babies under the age of one per 1,000 live births. There are many causes for infant mortality, which include birth defects, low birth weight, pregnancy complications, and sudden infant death syndrome. In order to decrease the high rates of infant mortality, there needs to be an increase in education and medicine so babies and mothers can receive the proper treatment needed. Maternal mortality is also related to infant mortality. If mothers can attend more prenatal visits and have more access to healthcare facilities, maternal mortality can decrease, and babies have a better chance of surviving in their first year. Worldwide infant mortality rates Infant mortality rates vary worldwide; however, some areas are more affected than others. Afghanistan suffered from the highest infant mortality rate in 2024, and the following 19 countries all came from Africa, with the exception of Pakistan. On the other hand, Slovenia had the lowest infant mortality rate that year. High infant mortality rates can be attributed to lack of sanitation, technological advancements, and proper natal care. In the United States, Massachusetts had the lowest infant mortality rate, while Mississippi had the highest in 2022. Overall, the number of neonatal and post neonatal deaths in the United States has been steadily decreasing since 1995.

BackgroundSudden infant death syndrome (SIDS) remains a leading cause of infant mortality globally. Although the global burden has generally declined over recent decades, the COVID-19 pandemic may have influenced these trends. This study investigates whether the global SIDS burden has changed, particularly during the COVID-19 pandemic.MethodsData from the Global Burden of Disease (GBD) 2021 study were analyzed to estimate SIDS mortality and disability-adjusted life years (DALYs) globally, regionally, and nationally. Rates were stratified by sex, age group, socio-demographic index (SDI), and health system level. Projections were made using the Bayesian Age-Period-Cohort model and the the autoregressive integrated moving average (ARIMA) model.ResultsIn 2021, global SIDS deaths totaled 30,608, with a mortality rate of 24.16 per 100,000 infants (95% UI, 14.06–32.44). Global DALYs were 2,746,174, at a rate of 2,167.56 per 100,000 infants (95% UI, 1,261.44–2,909.59). Mortality and DALYs rates decreased by 59% from 1990 to 2021, with marked regional differences. Regions with Low SDI and Minimal health systems, particularly Sub-Saharan Africa, had the highest burden, while higher SDI and advanced health system regions reported significant declines. Male infants aged 1–5 months showed higher rates than females. Despite a global decline during the pandemic, temporary increases occurred in countries including China, the Russian Federation, and Monaco. Projections suggest continued declines, predicting a mortality rate of 16.86 per 100,000 infants and DALYs rate of 1,400.41 per 100,000 infants by 2035.ConclusionsThe global SIDS burden has consistently declined since 1990, including during COVID-19, yet significant regional disparities remain. Enhanced healthcare interventions and targeted public health initiatives are crucial, particularly in regions with Low SDI and Minimal health system resources.

Data set from the article Lévesque V, Laplante L, Shohoudi A, Apers S, Kovacs AH, Luyckx K, Thomet C, Budts W, Enomoto J, Sluman MA, Lu CW, Jackson JL, Cook SC, Chidambarathanu S, Alday L, Eriksen K, Dellborg M, Berghammer M, Johansson B, Mackie AS, Menahem S, Caruana M, Veldtman G, Soufi A, Fernandes SM, White K, Callus E, Kutty S, Brouillette J, Casteigt B, Moons P, Khairy P; APPROACH-IS Consortium and the International Society for Adult Congenital Heart Disease (ISACHD). Implantable cardioverter-defibrillators and patient-reported outcomes in adults with congenital heart disease: An international study. Heart Rhythm. 2020 May;17(5 Pt A):768-776. doi: 10.1016/j.hrthm.2019.11.026. Epub 2019 Nov 30. PMID: 31790832.

This is the abstract:

Background: Implantable cardioverter-defibrillators (ICDs) are increasingly being used to prevent sudden death in the growing population of adults with congenital heart disease (CHD). However, little is known about their impact on patient-reported outcomes (PROs).

Objective: The purpose of this study was to assess and compare PROs in adults with CHD with and without ICDs.

Methods: A propensity-based matching weight analysis was conducted to evaluate PROs in an international cross-sectional study of adults with CHD from 15 countries across 5 continents.

Results: A total of 3188 patients were included: 107 with ICDs and 3081 weight-matched controls without ICDs. ICD recipients were an average age of 40.1 ± 12.4 years, and >95% had moderate or complex CHD. Defibrillators were implanted for primary and secondary prevention in 38.3% and 61.7%, respectively. Perceived health status, psychological distress, sense of coherence, and health behaviors did not differ significantly among patients with and without ICDs. However, ICD recipients had a more threatening view of their illness (relative % difference 8.56; P = .011). Those with secondary compared to primary prevention indications had a significantly lower quality-of-life score (Linear Analogue Scale 72.0 ± 23.1 vs 79.2 ± 13.0; P = .047). Marked geographic variations were observed. Overall sense of well-being, assessed by a summary score that combines various PROs, was significantly lower in ICD recipients (vs controls) from Switzerland, Argentina, Taiwan, and the United States.

Conclusion: In an international cohort of adults with CHD, ICDs were associated with a more threatening illness perception, with a lower quality of life in those with secondary compared to primary prevention indications. However, marked geographic variability in PROs was observed.