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).

Unintentional injuries claimed the lives of ******* Americans in 2023, marking a slight decrease from the previous year. This figure represents a significant public health concern. While heart disease and cancer typically top the list of mortality causes, unintentional injuries were the ***** leading cause of death in 2023. Poisoning emerges as a major contributor Among the various types of unintentional injuries, poisoning stands out as a particularly deadly threat. In 2023, approximately ******* deaths were attributed to unintentional poisoning, accounting for nearly half of all accident-related fatalities. That year, the death rate due to poisoning reached **** per 100,000 population. Unintentional drug overdose deaths account for a majority of poisoning deaths in the United States, with opioids, and in particular fentanyl, currently fueling these high numbers. Home safety remains a critical concern The home environment, often considered a safe haven, continues to be a significant site for unintentional injury-related deaths. In 2021 and 2022, the rate of such fatalities occurring at home peaked at **** per 100,000 population. Most unintentional injury deaths at home occur among elderly people, with falls accounting for the majority of such deaths among this age group.

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

The highest number of unintentional-injury-related deaths at home in the United States occurred in 2022 with ******* such deaths. This statistic shows a timeline of the number of unintentional-injury-related deaths at home in the United States from 1930 to 2023.

Annual data on unexpected deaths (certified by a coroner) and infant deaths by selected causes in England and Wales.

The highest rate of deaths due to falls in the United States was ** per 100,000 population in 1945. This statistic shows a timeline of the rate of unintentional-injury-related deaths due to falls in the United States from 1905 to 2023.

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.

The total number of deaths due to unintentional injuries in the United States was ******* in 2023. This statistic shows the number of unintentional-injury-related deaths by principal class in the United States from 2020 to 2023.

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

For the week ending August 29, 2025, weekly deaths in England and Wales were 985 below the number expected, compared with 855 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 highest rate of unintentional-injury-related deaths at home in the United States was **** per 100,000 population in 2021 and 2022. This statistic shows the rate of unintentional-injury-related deaths at home in the United States from 1930 to 2023, per every 100,000 population.

Annual data on sudden infant deaths in England and Wales and deaths for which the cause remained unascertained after a full investigation.

From 2016 to 2020, the rate of sudden infant death syndrome among Hispanics in the United States was **** 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.

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.

BackgroundWhile the high prevalence of preterm births and its impact on infant mortality in the US have been widely acknowledged, recent data suggest that even full-term births in the US face substantially higher mortality risks compared to European countries with low infant mortality rates. In this paper, we use the most recent birth records in the US to more closely analyze the primary causes underlying mortality rates among full-term births.Methods and findingsLinked birth and death records for the period 2010–2012 were used to identify the state- and cause-specific burden of infant mortality among full-term infants (born at 37–42 weeks of gestation). Multivariable logistic models were used to assess the extent to which state-level differences in full-term infant mortality (FTIM) were attributable to observed differences in maternal and birth characteristics. Random effects models were used to assess the relative contribution of state-level variation to FTIM. Hypothetical mortality outcomes were computed under the assumption that all states could achieve the survival rates of the best-performing states. A total of 10,175,481 infants born full-term in the US between January 1, 2010, and December 31, 2012, were analyzed. FTIM rate (FTIMR) was 2.2 per 1,000 live births overall, and ranged between 1.29 (Connecticut, 95% CI 1.08, 1.53) and 3.77 (Mississippi, 95% CI 3.39, 4.19) at the state level. Zero states reached the rates reported in the 6 low-mortality European countries analyzed (FTIMR < 1.25), and 13 states had FTIMR > 2.75. Sudden unexpected death in infancy (SUDI) accounted for 43% of FTIM; congenital malformations and perinatal conditions accounted for 31% and 11.3% of FTIM, respectively. The largest mortality differentials between states with good and states with poor FTIMR were found for SUDI, with particularly large risk differentials for deaths due to sudden infant death syndrome (SIDS) (odds ratio [OR] 2.52, 95% CI 1.86, 3.42) and suffocation (OR 4.40, 95% CI 3.71, 5.21). Even though these mortality differences were partially explained by state-level differences in maternal education, race, and maternal health, substantial state-level variation in infant mortality remained in fully adjusted models (SIDS OR 1.45, suffocation OR 2.92). The extent to which these state differentials are due to differential antenatal care standards as well as differential access to health services could not be determined due to data limitations. Overall, our estimates suggest that infant mortality could be reduced by 4,003 deaths (95% CI 2,284, 5,587) annually if all states were to achieve the mortality levels of the best-performing state in each cause-of-death category. Key limitations of the analysis are that information on termination rates at the state level was not available, and that causes of deaths may have been coded differentially across states.ConclusionsMore than 7,000 full-term infants die in the US each year. The results presented in this paper suggest that a substantial share of these deaths may be preventable. Potential improvements seem particularly large for SUDI, where very low rates have been achieved in a few states while average mortality rates remain high in most other areas. Given the high mortality burden due to SIDS and suffocation, policy efforts to promote compliance with recommended sleeping arrangements could be an effective first step in this direction.

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.

Financial overview and grant giving statistics of Sudden Cardiac-Death Awareness Research Foundation

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".