In the United States in 2021, the death rate was highest among those aged 85 and over, with about 17,190.5 men and 14,914.5 women per 100,000 of the population passing away. For all ages, the death rate was at 1,118.2 per 100,000 of the population for males, and 970.8 per 100,000 of the population for women. The death rate Death rates generally are counted as the number of deaths per 1,000 or 100,000 of the population and include both deaths of natural and unnatural causes. The death rate in the United States had pretty much held steady since 1990 until it started to increase over the last decade, with the highest death rates recorded in recent years. While the birth rate in the United States has been decreasing, it is still currently higher than the death rate. Causes of death There are a myriad number of causes of death in the United States, but the most recent data shows the top three leading causes of death to be heart disease, cancers, and accidents. Heart disease was also the leading cause of death worldwide.

Number of deaths and mortality rates, by age group, sex, and place of residence, 1991 to most recent year.

In 2023, the age-specific death rate for men aged 90 or over in England and Wales was 248.1 per one thousand population, and 215.1 for women. Except for infants that were under the age of one, younger age groups had the lowest death rate, with the death rate getting progressively higher in older age groups.

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.

Annual data on death registrations by single year of age for the UK (1974 onwards) and England and Wales (1963 onwards).

Between the beginning of January 2020 and June 14, 2023, of the 1,134,641 deaths caused by COVID-19 in the United States, around 307,169 had occurred among those aged 85 years and older. This statistic shows the number of coronavirus disease 2019 (COVID-19) deaths in the U.S. from January 2020 to June 2023, by age.

This table contains 2394 series, with data for years 1991 -1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Population group (19 items: Entire cohort; Income adequacy quintile 1 (lowest);Income adequacy quintile 3;Income adequacy quintile 2 ...), Age (14 items: At 25 years; At 30 years; At 35 years; At 40 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Probability of survival; Low 95% confidence interval; life expectancy; High 95% confidence interval; life expectancy ...).

Minimum to maximum probability of death (45q15) on different levels of assignment of deaths with missing ethnicity: from assignment based on population proportion (0% re-assignment), to 10%, 20%, 30%, 40%, and 50% death re-assignment.

The leading causes of death in the United States are heart disease and cancer. However, in 2022, COVID-19 was the fourth leading cause of death in the United States, accounting for around six percent of all deaths that year. In 2022, there were around 45 deaths from COVID-19 per 100,000 population.

Cardiovascular disease

Deaths from cardiovascular disease are more common among men than women but have decreased for both sexes over the past few decades. Coronary heart disease accounts for the highest portion of cardiovascular disease deaths in the United States, followed by stroke and high blood pressure. The states with the highest death rates from cardiovascular disease include Oklahoma, Mississippi, and Alabama. Smoking tobacco, physical inactivity, poor diet, stress, and being overweight or obese are all risk factors for developing heart disease.

Cancer

Although cancer is the second leading cause of death in the United States, like deaths from cardiovascular disease, deaths from cancer have decreased over the last few decades. The highest death rates from cancer come from lung cancer for both men and women. Breast cancer is the second deadliest cancer for women, while prostate cancer is the second deadliest cancer for men. West Virginia, Mississippi, and Kentucky lead the nation with the highest cancer death rates.

Probability of death between 15 and 60 years of age, years of life lost, and age-standardized mortality rate between between 15 and 65 years of age among women and men in Belize (2008–2010).

This dataset provides global estimates of adult mortality risk, 45q15 (probability of death between the ages of 15 years and 60 years), between 1970 and 2010.

Values in parentheses indicate lower and upper bounds of 95% CI.aA combination of high blood glucose, high LDL cholesterol, high blood pressure (directly, and indirectly through high dietary salt intake), and high body mass index.

Background: Substantial mortality occurs after hospital discharge in children younger than 5 years with suspected sepsis, especially in low-income countries. A better understanding of its epidemiology is needed for effective interventions to reduce child mortality in these countries. We evaluated risk factors for death after discharge in children admitted to hospital for suspected sepsis in Uganda, and assessed how these differed by age, time of death, and location of death. Methods: In this prospective observational cohort study, we recruited 0-60-month-old children admitted with suspected sepsis from the community to the paediatric wards of six Ugandan hospitals. The primary outcome was six-month post-discharge mortality among those discharged alive. We evaluated the interactive impact of age, time of death, and location of death on risk factors for mortality. Findings: 6,545 children were enrolled, with 6,191 discharged alive. The median (interquartile range) time from discharge to death was 28 (9-74) days, with a six-month post-discharge mortality rate of 5·5%, constituting 51% of total mortality. Deaths occurred at home (45%), in-transit to care (18%), or in hospital (37%) during a subsequent readmission. Post-discharge death was strongly associated with weight-for-age z-scores < -3 (adjusted risk ratio [aRR] 4·7, 95% CI 3·7–5·8 vs a Z score of >–2), referral for further care (7·3, 5·6–9·5), and unplanned discharge (3·2, 2·5–4·0). The hazard ratio of those with severe anaemia increased with time since discharge, while the hazard ratios of discharge vulnerabilities (unplanned, poor feeding) decreased with time. Age influenced the effect of several variables, including anthropometric indices (less impact with increasing age), anaemia (greater impact), and admission temperature (greater impact). Data Collection Methods: All data were collected at the point of care using encrypted study tablets and these data were then uploaded to a Research Electronic Data Capture (REDCap) database hosted at the BC Children’s Hospital Research Institute (Vancouver, Canada). At admission, trained study nurses systematically collected data on clinical, social and demographic variables. Following discharge, field officers contacted caregivers at 2 and 4 months by phone, and in-person at 6 months, to determine vital status, post-discharge health-seeking, and readmission details. Verbal autopsies were conducted for children who had died following discharge. Data Processing Methods: For this analysis, data from both cohorts (0-6 months and 6-60 months) were combined and analysed as a single dataset. We used periods of overlapping enrolment (72% of total enrolment months) between the two cohorts to determine site-specific proportions of children who were 0-6 and 6-60 months of age. These proportions were used to weight the cohorts for the calculation of overall mortality rate. Z-scores were calculated using height and weight. Hematocrit was converted to hemoglobin. Distance to hospital was calculated using latitude and longitude. Extra symptom and diagnosis categories were created based on text field in these two variables. BCS score was created by summing all individual components. Abbreviations: MUAC -mid upper arm circumference wfa – weight for age wfl – weight for length bmi – body mass index lfa – length for age abx - antibiotics hr – heart rate rr – respiratory rate antimal - antimalarial sysbp – systolic blood pressure diasbp – diastolic blood pressure resp – respiratory cap - capillary BCS - Blantyre Coma Scale dist- distance hos - hospital ed - education disch - discharge dis -discharge fu – follow-up pd – post-discharge loc - location materl - maternal Ethics Declaration: This study was approved by the Mbarara University of Science and Technology Research Ethics Committee (No. 15/10-16), the Uganda National Institute of Science and Technology (HS 2207), and the University of British Columbia / Children & Women’s Health Centre of British Columbia Research Ethics Board (H16-02679). This manuscript adheres to the guidelines for STrengthening the Reporting of OBservational studies in Epidemiology (STROBE). Study Protocol & Supplementary Materials: Smart Discharges to improve post-discharge health outcomes in children: A prospective before-after study with staggered implementation, NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

During the 2023-2024 flu season in the United States, an estimated 27,965 people died from influenza. The vast majority of deaths due to influenza occur among the elderly, with those aged 65 years and older accounting for 19,038 deaths during the 2023-2024 flu season. During this time, the mortality rate from influenza among those aged 65 years and older was around 32 per 100,000 population, compared to a mortality rate of two per 100,000 population among those aged 18 to 49 years. Influenza deaths Although most people recover from influenza without the need of medical care, influenza and pneumonia are still major causes of death in the United States. Influenza is a common cause of pneumonia and cases in which influenza develops into pneumonia tend to be more severe and more deadly. However, the impact of influenza varies from year to year depending on which viruses are circulating. For example, during the 2017-2018 flu season around 52,000 people died due to influenza, whereas in 2023-2024 total deaths amounted to 28,000. Preventing death The most effective way to prevent influenza is to receive an annual influenza vaccination. These vaccines have proven to be safe and are usually cheap and easily accessible. Each year, flu vaccinations prevent thousands of influenza cases, hospitalizations and deaths. It was estimated that during the 2022-2023 flu season, vaccinations prevented the deaths of around 2,479 people aged 65 years and older.

Figure S1 Comparison of death rates in RealAge sample to US death rates. Figure S2 RealAge delta in California sample with full identifiers. Figure S3 RealAge delta in RealAge population. Figure S4 RealAge delta in California sample with full identifiers - age 25 to 44. Figure S5 RealAge delta in California sample with full identifiers - age 45 to 64. Figure S6 RealAge delta in California sample with full identifiers - age 65 to 84. Figure S7 Calibration plot - Age. Figure S8 Calibration plot - RealAge. Figure S9 Calibration plot - Framingham. Figure S10 Calibration plot - Framingham (unscaled age). Figure S11 Calibration plot - Framingham (unscaled age, indicators only). Figure S12 Comparison of RealAge score distributions for all deceased/surviving users and deceased/surviving “healthy” users (heart disease). Figure S13 Comparison of RealAge score distributions for all deceased/surviving users and deceased/surviving “healthy” users (cancer). Figure S14 Comparison of RealAge score distributions for all deceased/surviving users and deceased/surviving “healthy” users (external causes). Table S1 25 to 44 - causes of death. Table S2 45 to 64 - causes of death. Table S3 65 to 84 - causes of death. Table S4 Log-Likelihood for Table 4 Comparison of Age and RealAge. Table S5 Framingham Hard Coronary Heart Disease 10-Year Risk Model results–unscaled age. Table S6 Framingham Hard Coronary Heart Disease 10-Year Risk Model results–indicators only, unscaled age. Table S7 25 to 44 - model results. Table S8 25 to 44 - model results (age base). Table S9 45 to 64 - model results. Table S10 45 to 64 - model results (age base). Table S11 65 to 84 - model results. Table S12 65 to 84 - model results (age base). Table S13 All users - Death from Heart Disease. Table S14 Death from Heart Disease (age in base hazard). Table S15 All users - Death from Cancer. Table S16 Death from Cancer (age in base hazard). Table S17 All users - Death from External Cause. Table S18 Death from External Cause (age in base hazard). Table S19 All users - at least two years follow-up. Table S20 All users - Death from Heart Disease, no Report Diagnosis or Interest for Heart Attack or Stroke, no Smoking. Table S21 All users - Death from Cancer, no Reported Diagnosis or Interest for Cancer, no Smoking. Table S22 All users - Death from External Cause, no Reported Diagnosis or Interest for Depression. (PDF)

Number and percentage of deaths, by age group, sex, and place of residence, 1991 to most recent year.

As of April 26, 2023, around 27 percent of total COVID-19 deaths in the United States have been among adults 85 years and older, despite this age group only accounting for two percent of the U.S. population. This statistic depicts the distribution of total COVID-19 deaths in the United States as of April 26, 2023, by age group.

IntroductionThe current study sought to explore the effect of baseline serum uric acid (SUA) on the risk of all-cause mortality among Chinese adults aged 45~75 years and to determine its interaction relationship with diabetes.MethodsThe study was designed as a community-based cohort of 4467 adults aged between 45~75 years included in a 6-years follow-up period from 2009 to 2015 years by the China Health and Nutrition Survey (CHNS). Baseline SUA levels were grouped into quartiles and its association on all-cause mortality was explored using multivariate Cox proportional hazards models. Stratified analyses were performed to explore the associations of SUA quartiles with all-cause mortality among diabetic and non-diabetic individuals.ResultsA total of 141 deaths (5.3 per 1000 person-years) were recorded During a follow-up of 26431 person-years. Out of the 141 deaths, 28 deaths (10.1 per 1000 person-years) were reported in the diabetic groups and 113 deaths (4.8 per 1000 person-years) were recorded in the non-diabetic group. An increased risk of all-cause mortality was observed for participants in the first and fourth quartiles compared with the second SUA quartile, (Q1 SUA: aHR=2.1, 95% CI 1.1~4.1; Q4 SUA: aHR=2.1, 95% CI 1.1~4.0). Stratification of participants by diabetes status showed a U-shaped association for non-diabetic individuals. Whereas, declined eGFR, rather than SUA, was an independent risk factor for all-cause mortality in diabetic individuals (aHR=0.7, 95% CI 0.6~1.0).ConclusionOur study proved that the prognostic role of SUA for predicting all-cause death might be regulated by diabetes. Both low and high SUA levels were associated with increased mortality, supporting a U-shaped association only in non-diabetic individuals. Whereas, renal dysfunction rather than SUA was an independent risk factor for all-cause mortality. Further studies should be conducted to determine the SUA levels at which intervention should be conducted and explore target follow-up strategies to prevent progression leading to poor prognosis.

Objective: A one third reduction of premature deaths from non-communicable diseases by 2030 is a target of the United Nations Sustainable Development Goal for Health. Unlike in other developed nations, premature mortality in the United States (US) is increasing. The state of Oklahoma suffers some of the greatest rates in the US of both all-cause mortality and overdose deaths. Medicaid opioids are associated with overdose death at the patient level, but the impact of this exposure on population all-cause mortality is unknown. The objective of this study was to look for an association between Medicaid spending, as proxy measure for Medicaid opioid exposure, and all-cause mortality rates in the 45–54-year-old American Indian/Alaska Native (AI/AN45-54) and non-Hispanic white (NHW45-54) populations.Methods: All-cause mortality rates were collected from the US Centers for Disease Control & Prevention Wonder Detailed Mortality database. Annual per capita (APC) Medicaid spending, and APC Medicare opioid claims, smoking, obesity, and poverty data were also collected from existing databases. County-level multiple linear regression (MLR) analyses were performed. American Indian mortality misclassification at death is known to be common, and sparse populations are present in certain counties; therefore, the two populations were examined as a combined population (AI/NHW45-54), with results being compared to NHW45-54 alone.Results: State-level simple linear regressions of AI/NHW45-54 mortality and APC Medicaid spending show strong, linear correlations: females, coefficient 0.168, (R2 0.956; P < 0.0001; CI95 0.15, 0.19); and males, coefficient 0.139 (R2 0.746; P < 0.0001; CI95 0.10, 0.18). County-level regression models reveal that AI/NHW45-54 mortality is strongly associated with APC Medicaid spending, adjusting for Medicare opioid claims, smoking, obesity, and poverty. In females: [R2 0.545; (F)P < 0.0001; Medicaid spending coefficient 0.137; P < 0.004; 95% CI 0.05, 0.23]. In males: [R2 0.719; (F)P < 0.0001; Medicaid spending coefficient 0.330; P < 0.001; 95% CI 0.21, 0.45].Conclusions: In Oklahoma, per capita Medicaid spending is a very strong risk factor for all-cause mortality in the combined AI/NHW45-54 population, after controlling for Medicare opioid claims, smoking, obesity, and poverty.

BackgroundTobacco smoking is a major risk factor for many diseases. We sought to quantify the burden of tobacco-smoking-related deaths in Asia, in parts of which men's smoking prevalence is among the world's highest.Methods and FindingsWe performed pooled analyses of data from 1,049,929 participants in 21 cohorts in Asia to quantify the risks of total and cause-specific mortality associated with tobacco smoking using adjusted hazard ratios and their 95% confidence intervals. We then estimated smoking-related deaths among adults aged ≥45 y in 2004 in Bangladesh, India, mainland China, Japan, Republic of Korea, Singapore, and Taiwan—accounting for ∼71% of Asia's total population. An approximately 1.44-fold (95% CI = 1.37–1.51) and 1.48-fold (1.38–1.58) elevated risk of death from any cause was found in male and female ever-smokers, respectively. In 2004, active tobacco smoking accounted for approximately 15.8% (95% CI = 14.3%–17.2%) and 3.3% (2.6%–4.0%) of deaths, respectively, in men and women aged ≥45 y in the seven countries/regions combined, with a total number of estimated deaths of ∼1,575,500 (95% CI = 1,398,000–1,744,700). Among men, approximately 11.4%, 30.5%, and 19.8% of deaths due to cardiovascular diseases, cancer, and respiratory diseases, respectively, were attributable to tobacco smoking. Corresponding proportions for East Asian women were 3.7%, 4.6%, and 1.7%, respectively. The strongest association with tobacco smoking was found for lung cancer: a 3- to 4-fold elevated risk, accounting for 60.5% and 16.7% of lung cancer deaths, respectively, in Asian men and East Asian women aged ≥45 y.ConclusionsTobacco smoking is associated with a substantially elevated risk of mortality, accounting for approximately 2 million deaths in adults aged ≥45 y throughout Asia in 2004. It is likely that smoking-related deaths in Asia will continue to rise over the next few decades if no effective smoking control programs are implemented.Please see later in the article for the Editors' Summary