This statistic shows the number of deaths from the leading causes of death among U.S. adults aged 65 years and older in 2014, per 100,000 population. In 2014, the leading cause of death among those aged 65 years and older was heart disease, followed by cancer and chronic lower respiratory diseases.

MMWR Surveillance Summary 66 (No. SS-1):1-8 found that nonmetropolitan areas have significant numbers of potentially excess deaths from the five leading causes of death. These figures accompany this report by presenting information on potentially excess deaths in nonmetropolitan and metropolitan areas at the state level. They also add additional years of data and options for selecting different age ranges and benchmarks. Potentially excess deaths are defined in MMWR Surveillance Summary 66(No. SS-1):1-8 as deaths that exceed the numbers that would be expected if the death rates of states with the lowest rates (benchmarks) occurred across all states. They are calculated by subtracting expected deaths for specific benchmarks from observed deaths. Not all potentially excess deaths can be prevented; some areas might have characteristics that predispose them to higher rates of death. However, many potentially excess deaths might represent deaths that could be prevented through improved public health programs that support healthier behaviors and neighborhoods or better access to health care services. Mortality data for U.S. residents come from the National Vital Statistics System. Estimates based on fewer than 10 observed deaths are not shown and shaded yellow on the map. Underlying cause of death is based on the International Classification of Diseases, 10th Revision (ICD-10) Heart disease (I00-I09, I11, I13, and I20–I51) Cancer (C00–C97) Unintentional injury (V01–X59 and Y85–Y86) Chronic lower respiratory disease (J40–J47) Stroke (I60–I69) Locality (nonmetropolitan vs. metropolitan) is based on the Office of Management and Budget’s 2013 county-based classification scheme. Benchmarks are based on the three states with the lowest age and cause-specific mortality rates. Potentially excess deaths for each state are calculated by subtracting deaths at the benchmark rates (expected deaths) from observed deaths. Users can explore three benchmarks: “2010 Fixed” is a fixed benchmark based on the best performing States in 2010. “2005 Fixed” is a fixed benchmark based on the best performing States in 2005. “Floating” is based on the best performing States in each year so change from year to year. SOURCES CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES Moy E, Garcia MC, Bastian B, Rossen LM, Ingram DD, Faul M, Massetti GM, Thomas CC, Hong Y, Yoon PW, Iademarco MF. Leading Causes of Death in Nonmetropolitan and Metropolitan Areas – United States, 1999-2014. MMWR Surveillance Summary 2017; 66(No. SS-1):1-8. Garcia MC, Faul M, Massetti G, Thomas CC, Hong Y, Bauer UE, Iademarco MF. Reducing Potentially Excess Deaths from the Five Leading Causes of Death in the Rural United States. MMWR Surveillance Summary 2017; 66(No. SS-2):1–7.

This dataset contains information on the number of deaths and age-adjusted death rates for the five leading causes of death in 1900, 1950, and 2000.

Age-adjusted death rates (deaths per 100,000) after 1998 are calculated based on the 2000 U.S. standard population. Populations used for computing death rates for 2011–2015 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years between 2000 and 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Data on age-adjusted death rates prior to 1999 are taken from historical data (see References below).

SOURCES

CDC/NCHS, National Vital Statistics System, historical data, 1900-1998 (see https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm); CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov).

REFERENCES

1. National Center for Health Statistics, Data Warehouse. Comparability of cause-of-death between ICD revisions. 2008. Available from: http://www.cdc.gov/nchs/nvss/mortality/comparability_icd.htm.

2. National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm.

3. Murphy SL, Xu JQ, Kochanek KD, Curtin SC, and Arias E. Deaths: Final data for 2015. National vital statistics reports; vol 66. no. 6. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_06.pdf.

4. Arias E, Heron M, and Xu JQ. United States life tables, 2014. National vital statistics reports; vol 66 no 4. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_04.pdf.

5. National Center for Health Statistics. Historical Data, 1900-1998. 2009. Available from: https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm.

Analysis of ‘NCHS - Potentially Excess Deaths from the Five Leading Causes of Death’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/3d1da62a-9f1c-47e8-b5a1-b473f57d7fdc on 28 January 2022.

--- Dataset description provided by original source is as follows ---

MMWR Surveillance Summary 66 (No. SS-1):1-8 found that nonmetropolitan areas have significant numbers of potentially excess deaths from the five leading causes of death. These figures accompany this report by presenting information on potentially excess deaths in nonmetropolitan and metropolitan areas at the state level. They also add additional years of data and options for selecting different age ranges and benchmarks.

Potentially excess deaths are defined in MMWR Surveillance Summary 66(No. SS-1):1-8 as deaths that exceed the numbers that would be expected if the death rates of states with the lowest rates (benchmarks) occurred across all states. They are calculated by subtracting expected deaths for specific benchmarks from observed deaths.

Not all potentially excess deaths can be prevented; some areas might have characteristics that predispose them to higher rates of death. However, many potentially excess deaths might represent deaths that could be prevented through improved public health programs that support healthier behaviors and neighborhoods or better access to health care services.

Mortality data for U.S. residents come from the National Vital Statistics System. Estimates based on fewer than 10 observed deaths are not shown and shaded yellow on the map.

Underlying cause of death is based on the International Classification of Diseases, 10th Revision (ICD-10)

Heart disease (I00-I09, I11, I13, and I20–I51) Cancer (C00–C97) Unintentional injury (V01–X59 and Y85–Y86) Chronic lower respiratory disease (J40–J47) Stroke (I60–I69) Locality (nonmetropolitan vs. metropolitan) is based on the Office of Management and Budget’s 2013 county-based classification scheme.

Benchmarks are based on the three states with the lowest age and cause-specific mortality rates.

Potentially excess deaths for each state are calculated by subtracting deaths at the benchmark rates (expected deaths) from observed deaths.

Users can explore three benchmarks:

“2010 Fixed” is a fixed benchmark based on the best performing States in 2010. “2005 Fixed” is a fixed benchmark based on the best performing States in 2005. “Floating” is based on the best performing States in each year so change from year to year.

SOURCES

CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov).

REFERENCES

1. Moy E, Garcia MC, Bastian B, Rossen LM, Ingram DD, Faul M, Massetti GM, Thomas CC, Hong Y, Yoon PW, Iademarco MF. Leading Causes of Death in Nonmetropolitan and Metropolitan Areas – United States, 1999-2014. MMWR Surveillance Summary 2017; 66(No. SS-1):1-8.

2. Garcia MC, Faul M, Massetti G, Thomas CC, Hong Y, Bauer UE, Iademarco MF. Reducing Potentially Excess Deaths from the Five Leading Causes of Death in the Rural United States. MMWR Surveillance Summary 2017; 66(No. SS-2):1–7.

--- Original source retains full ownership of the source dataset ---

This dataset of U.S. mortality trends since 1900 highlights trends in age-adjusted death rates for five selected major causes of death.

Age-adjusted death rates (deaths per 100,000) after 1998 are calculated based on the 2000 U.S. standard population. Populations used for computing death rates for 2011–2015 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years between 2000 and 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Data on age-adjusted death rates prior to 1999 are taken from historical data (see References below).

Revisions to the International Classification of Diseases (ICD) over time may result in discontinuities in cause-of-death trends.

SOURCES

CDC/NCHS, National Vital Statistics System, historical data, 1900-1998 (see https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm); CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov).

REFERENCES

1. National Center for Health Statistics, Data Warehouse. Comparability of cause-of-death between ICD revisions. 2008. Available from: http://www.cdc.gov/nchs/nvss/mortality/comparability_icd.htm.

2. National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm.

3. Murphy SL, Xu JQ, Kochanek KD, Curtin SC, and Arias E. Deaths: Final data for 2015. National vital statistics reports; vol 66. no. 6. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_06.pdf.

4. Arias E, Heron M, and Xu JQ. United States life tables, 2014. National vital statistics reports; vol 66 no 4. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_04.pdf.

5. National Center for Health Statistics. Historical Data, 1900-1998. 2009. Available from: https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm.

The leading causes of death by sex and ethnicity in New York City in since 2007.

This statistic shows the number of gun deaths in the United States annually as an average from the years 2012 to 2014, by cause. Suicide was the largest cause of gun deaths in the United States. On average, ****** people in the United States took their own life using a firearm each year.

The share of the population with overweight in the United States was forecast to continuously increase between 2024 and 2029 by in total 1.6 percentage points. After the fifteenth consecutive increasing year, the overweight population share is estimated to reach 77.43 percent and therefore a new peak in 2029. Notably, the share of the population with overweight of was continuously increasing over the past years.Overweight is defined as a body mass index (BMI) of more than 25.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the share of the population with overweight in countries like Canada and Mexico.

In 2014, ** percent of U.S. citizens opposing the death penalty stated they think it is wrong to take a life, while ** percent said that punishment should be left to God.

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 dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

The statistic represents the most common reasons to support the death penalty in the United States in 2014. In 2014, the biblical phrase "an eye for an eye" is the most popular reason for Americans to support the death penalty. About 35 percent of respondents stated this phrase as the main reason for their support.

Background: Death with graft function remains an important cause of graft loss among kidney transplant recipients (KTRs). Little is known about the trend of specific causes of death in KTRs in recent years. Methods: We analyzed United States Renal Data System data (1996–2014) to determine 1- and 10-year all-cause and cause-specific mortality in adult KTRs who died with a functioning allograft. We also studied 1- and 10-year trends in the various causes of mortality. Results: Of 210,327 KTRs who received their first kidney transplant from 1996 to 2014, 3.2% died within 1 year after transplant. Cardiovascular deaths constituted the majority (24.7%), followed by infectious (15.2%) and malignant (2.9%) causes; 40.1% of deaths had no reported cause. Using 1996 as the referent year, all-cause as well as cardiovascular mortality declined, whereas mortality due to malignancy did not. For analyses of 10-year mortality, we studied 94,384 patients who received a first kidney transplant from 1996 to 2005. Of those, 22.1% died over 10 years and the causative patterns of their causes of death were similar to those associated with 1-year mortality. Conclusions: Despite the downtrend in mortality over the last 2 decades, a significant percentage of KTRs die in 10-years with a functioning graft, and cardiovascular mortality remains the leading cause of death. These data also highlight the need for diligent collection of mortality data in KTRs.

IntroductionInconsistencies of reports contributes to the underreporting of Alzheimer’s disease (AD) on death certificates. Whether underreporting exists within South Carolina has not been studied.MethodsWe conducted a prospective, population-based study on a cohort of persons (N = 78,534) previously diagnosed with AD and died between 2014–2019. We linked vital records with the South Carolina Alzheimer’s Disease and Related Dementias Registry to investigate their cause of death and survival rates. Descriptive analyses calculated frequencies of demographic and health-related characteristics. Turnbull’s method estimated the survival probabilities for different subgroups of patients. Hazard ratios were computed from the Cox proportional hazards model, adjusting for the following confounding variables of age at diagnosis, education level, gender, and race.ResultsThe top immediate cause of death was Alzheimer’s disease among all racial groups, except for Native American/American Indian. More females (60.3%) were affected by AD compared to males (39.7%). There is a 25% probability of survival, beyond 5 years, after AD diagnosis. Black/African American AD patients have the smallest risk of all-cause mortality across all racial/ethnic groups (HR 0.87; 95% CI, 0.85–0.89). Individuals with lower education had a lower likelihood of mortality.ConclusionAlthough AD was not underreported in the state of South Carolina further research is needed to develop protocols around classification of deaths among those diagnosed with dementia and comorbidities, including cardiovascular disease, to ensure dementia is properly reported as we move to prevent and treat Alzheimer’s disease by 2025 and beyond.

Since 2014, the most common known cause of death for migrants in transit in the Americas has been drowning, followed by vehicle accidents or deaths liked to hazardous transport. The U.S.-Mexico border is the most deadly route, as ***** migrants have been recorded dead or missing trying to cross it. The real figures of deaths and missing people are expected to be considerably higher, as the source warns about the difficulties and challenges of collecting this data, especially in Mexico, the Darien Gap, and maritime routes.

The U.S. Geological Survey National Wildlife Health Center (NWHC) measured environmental contaminants in bald eagles (Haliaeetus leucocephalus) and golden eagles (Aquila chrysaetos) to evaluate dietary exposure to lead, mercury, and anticoagulant rodenticides (AR), which was identified by U.S. Fish and Wildlife Service (USFWS) as a priority issue of concern for the Mountain Prairie Region 6. Carcasses of bald eagles (n = 172) and golden eagles (n = 142) collected from North and South Dakota, Montana, Wyoming, Colorado, Utah, Nebraska, and Kansas between 2014-2017 were assessed for cause of death and liver lead, mercury, and AR levels. Trauma, electrocution, and lead poisoning were the 3 leading causes of death, affecting 51%, 21%, and 20% of eagles, respectively. Trauma was the leading cause of death for both species, while lead poisoning was the second leading cause of death for bald eagles (31%) and was only diagnosed as the cause of death in 7% of golden eagles. Elevated lead levels within the range of subclinical or clinical poisoning (>2 mg/kg wet weight) were present in 25% of eagles tested, including 36% of bald eagles and 11% of golden eagles. No association was detected between lead exposure and trauma, electrocution, or infectious disease. Mercury levels were considered high (>80 mg per kilogram dry weight) for only 2% of bald eagles and no golden eagles. Brodifacoum was the most common AR detected, present in 56% of eagles, including 70% of bald eagles and 39% of golden eagles. However, death was not directly attributed to AR toxicosis in any case.

This statistic shows the number of deaths per day by selected diseases in West African countries that are suffering from the Ebola outbreak in 2014. Malaria causes some 552 deaths per day in these countries, while Ebola causes around four deaths per day (as of August 2014).

Ebola compared to other diseases

Ebola first emerged in 1976 in Sudan and the Democratic Republic of Congo. The 2014 outbreak in West Africa has proven difficult to control. Currently, there is no cure, however, treatment is available to maximize survival chances as well as minimize the potential for transmission. In August 2014, the World Health Organization has stated that the Ebola outbreak in West Africa had become an international health emergency. Ebola has caused four deaths per day in West Africa between December 2013 and August 11th, 2014. However, diseases such as malaria and HIV or AIDS have caused a significantly larger number of deaths daily in these countries, reaching 552 and 685 deaths per day in 2014, respectively. HIV/AIDS was responsible for some 1.5 million deaths in 2013 globally.

As of 2013, there have been over 77 million cases of malaria in Africa and almost 7 million cases in the Eastern Mediterranean. Worldwide, malaria accounted for just under 90 million cases in 2013. Malaria is caused by a parasite which can be carried by mosquitoes and transmitted to humans. The parasite is then able to multiply within the liver and proceed to infect red blood cells. Common symptoms are fever, headache, and vomiting. Malaria can cause death if blood supply to vital organs is inhibited. The U.S. National Institute of Health and the Bill & Melinda Gates Foundation are among the leading funders for malaria research and development worldwide, contributing to 27.9 percent and 21.2 percent, respectively, between 2007 and 2012.

We aimed to evaluate the association between leisure-time physical activity (PA) and mortality risk in adults with major chronic diseases. A total of 170,579 adults with major chronic diseases aged 30–84 years from the U.S. National Health Interview Surveys (1997–2014) with linkage to the National Death Index (NDI) through December 31, 2015 were included in this study. During a median follow-up of 7.25 years, 36,914 adults with chronic diseases died from all causes, 8,767 died from cardiovascular disease (CVD), and 9,090 died from cancer. Compared with participants with no leisure-time PA, those with a low level (10–59 min/week) of total leisure-time PA had a 23% [hazard ratio (HR) 0.77, 95% confidence interval (CI) 0.73–0.82] reduced risk of all-cause mortality. Adults with higher levels of leisure time had more reduced risk of all-cause mortality, as well as CVD-specific and cancer-specific mortality. Adults with leisure-time PA ≥ 1,500 min/week had more reduced risk of CVD-specific mortality (61%) but less reduced risk of cancer-specific mortality (29%) compared with the reduced risk of all-cause mortality (43%). There was an inversely non-linear dose-response relationship between leisure-time PA and all-cause and cause-specific mortality. Reduced risk of all-cause and cancer-specific mortality between leisure-time light-to-moderate PA and vigorous-intensity PA time were largely comparable. Low and high levels of leisure-time PA showed substantial survival benefits compared with no leisure-time PA in adults with major chronic diseases. The light-to-moderate-intensity leisure-time PA is largely comparable with vigorous PA to provide survival benefits for all-cause and cancer-specific mortality.

In 2023, around 72,776 people in the United States died from a drug overdose that involved fentanyl. This was the second-highest number of fentanyl overdose deaths ever recorded in the United States, and a significant increase from the number of deaths reported in 2019. Fentanyl overdoses are now the driving force behind the opioid epidemic, accounting for the majority of overdose deaths in the United States. What is fentanyl? Fentanyl is an extremely potent synthetic opioid similar to morphine, but more powerful. It is a prescription drug but is also manufactured illegally and is sometimes mixed with other illicit drugs such as heroin and cocaine, often without the user’s knowledge. The potency of fentanyl makes it very addictive and puts users at a high risk for overdose. Illegally manufactured fentanyl has become more prevalent in the United States in recent years, leading to a huge increase in drug overdose deaths. In 2022, the rate of drug overdose death involving fentanyl was 22.7 per 100,000 population, compared to a rate of just one per 100,000 population in the year 2013. Fentanyl overdoses by gender and race/ethnicity As of 2022, the rate of drug overdose deaths involving fentanyl in the United States is over two times higher among men than women. Rates of overdose death involving fentanyl were low for both men and women until around the year 2014 when they began to quickly increase, especially for men. In 2022, there were around 19,880 drug overdose deaths among women that involved fentanyl compared to 53,958 such deaths among men. At that time, the rate of fentanyl overdose deaths was highest among non-Hispanic American Indian or Alaska Natives and lowest among non-Hispanic Asians. However, from the years 2014 to 2018, non-Hispanic whites had the highest fentanyl overdose death rates.