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.

Canada CA: Probability of Dying at Age 10-14 Years: per 1000 data was reported at 0.500 Ratio in 2019. This records a decrease from the previous number of 0.600 Ratio for 2018. Canada CA: Probability of Dying at Age 10-14 Years: per 1000 data is updated yearly, averaging 0.700 Ratio from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 1.100 Ratio in 1991 and a record low of 0.500 Ratio in 2019. Canada CA: Probability of Dying at Age 10-14 Years: per 1000 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank.WDI: Health Statistics. Probability of dying between age 10-14 years of age expressed per 1,000 adolescents age 10, if subject to age-specific mortality rates of the specified year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

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.

India IN: Probability of Dying at Age 15-19 Years: per 1000 data was reported at 4.000 Ratio in 2019. This records a decrease from the previous number of 4.100 Ratio for 2018. India IN: Probability of Dying at Age 15-19 Years: per 1000 data is updated yearly, averaging 7.800 Ratio from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 10.500 Ratio in 1990 and a record low of 4.000 Ratio in 2019. India IN: Probability of Dying at Age 15-19 Years: per 1000 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Health Statistics. Probability of dying between age 15-19 years of age expressed per 1,000 adolescents age 15, if subject to age-specific mortality rates of the specified year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

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.

In 2022, the leading causes of death among children and adolescents in the United States aged 10 to 14 were unintentional injuries, intentional self-harm (suicide), and cancer. That year, unintentional injuries accounted for around 25 percent of all deaths among this age group. Leading causes of death among older teens Like those aged 10 to 14 years, the leading cause of death among older teenagers in the U.S. aged 15 to 19 years is unintentional injuries. In 2022, unintentional injuries accounted for around 37 percent of all deaths among older teens. However, unlike those aged 10 to 14, the second leading cause of death among teens aged 15 to 19 is assault or homicide. Sadly, the third leading cause of death among this age group is suicide, making suicide among the leading three causes of death for both age groups. Teen suicide Suicide remains a major problem among teenagers in the United States, as reflected in the leading causes of death among this age group. It was estimated that in 2021, around 22 percent of high school students in the U.S. considered attempting suicide in the past year, with this rate twice as high for girls than for boys. The states with the highest death rates due to suicide among adolescents aged 15 to 19 years are Montana, South Dakota, and New Mexico. In 2022, the death rate from suicide among this age group in Montana was 39 per 100,000 population. In comparison, New York, the state with the lowest rate, had just five suicide deaths among those aged 15 to 19 years per 100,000 population.

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 2;Income adequacy quintile 3 ...), Age (14 items: At 25 years; At 30 years; At 40 years; At 35 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Life expectancy; High 95% confidence interval; life expectancy; Low 95% confidence interval; life expectancy ...).

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

As of 2022, the third leading cause of death among teenagers aged 15 to 19 years in the United States was intentional self-harm or suicide, contributing around 17 percent of deaths among age group. The leading cause of death at that time was unintentional injuries, contributing to around 37.4 percent of deaths, while 21.8 percent of all deaths in this age group were due to assault or homicide. Cancer and heart disease, the overall leading causes of death in the United States, are also among the leading causes of death among U.S. teenagers. Adolescent suicide in the United States In 2021, around 22 percent of students in grades 9 to 12 reported that they had seriously considered attempting suicide in the past year. Female students were around twice as likely to report seriously considering suicide compared to male students. In 2022, Montana had the highest rate of suicides among U.S. teenagers with around 39 deaths per 100,000 teenagers, followed by South Dakota with a rate of 33 per 100,000. The states with the lowest death rates among adolescents are New York and New Jersey. Mental health treatment Suicidal thoughts are a clear symptom of mental health issues. Mental health issues are not rare among children and adolescents, and treatment for such issues has become increasingly accepted and accessible. In 2021, around 15 percent of boys and girls aged 5 to 17 years had received some form of mental health treatment in the past year. At that time, around 35 percent of youths aged 12 to 17 years in the United States who were receiving specialty mental health services were doing so because they had thought about killing themselves or had already tried to kill themselves.

Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Vaccination Status. Click 'More' for important dataset description and footnotes

Dataset and data visualization details: These data were posted on October 21, 2022, archived on November 18, 2022, and revised on February 22, 2023. These data reflect cases among persons with a positive specimen collection date through September 24, 2022, and deaths among persons with a positive specimen collection date through September 3, 2022.

Vaccination status: A person vaccinated with a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. Additional or booster dose: A person vaccinated with a primary series and an additional or booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after receipt of an additional or booster dose of any COVID-19 vaccine on or after August 13, 2021. For people ages 18 years and older, data are graphed starting the week including September 24, 2021, when a COVID-19 booster dose was first recommended by CDC for adults 65+ years old and people in certain populations and high risk occupational and institutional settings. For people ages 12-17 years, data are graphed starting the week of December 26, 2021, 2 weeks after the first recommendation for a booster dose for adolescents ages 16-17 years. For people ages 5-11 years, data are included starting the week of June 5, 2022, 2 weeks after the first recommendation for a booster dose for children aged 5-11 years. For people ages 50 years and older, data on second booster doses are graphed starting the week including March 29, 2022, when the recommendation was made for second boosters. Vertical lines represent dates when changes occurred in U.S. policy for COVID-19 vaccination (details provided above). Reporting is by primary series vaccine type rather than additional or booster dose vaccine type. The booster dose vaccine type may be different than the primary series vaccine type. ** Because data on the immune status of cases and associated deaths are unavailable, an additional dose in an immunocompromised person cannot be distinguished from a booster dose. This is a relevant consideration because vaccines can be less effective in this group. Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Rates of COVID-19 deaths by vaccination status are reported based on when the patient was tested for COVID-19, not the date they died. Deaths usually occur up to 30 days after COVID-19 diagnosis. Participating jurisdictions: Currently, these 31 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (New York), North Carolina, Philadelphia (Pennsylvania), Rhode Island, South Dakota, Tennessee, Texas, Utah, Washington, and West Virginia; 30 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 72% of the total U.S. population and all ten of the Health and Human Services Regions. Data on cases

Effective June 28, 2023, this dataset will no longer be updated. Similar data are accessible from CDC WONDER (https://wonder.cdc.gov/mcd-icd10-provisional.html).

Deaths involving coronavirus disease 2019 (COVID-19) with a focus on ages 0-18 years in the United States.

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.

The mortality rate from influenza in the United States is by far highest among those aged 65 years and older. During the 2023-2024 flu season, the mortality rate from influenza for this age group was around 32.1 per 100,000 population. The burden of influenza The impact of influenza in the U.S. varies from season to season, but in the 2023-2024 flu season, there were an estimated 40 million cases. These cases resulted in around 470,000 hospitalizations. Although most people recover from influenza without requiring medical treatment, the disease can be deadly for young children, the elderly, and those with weakened immune systems or chronic illnesses. During the 2023-2024 flu season, around 28,000 people in the U.S. lost their lives due to influenza. Impact of vaccinations The most effective way to prevent influenza is to receive an annual vaccination at the beginning of flu season. Flu vaccines are safe and can greatly reduce the burden of the disease. During the 2022-2023 flu season, vaccinations prevented around 2,479 deaths among those aged 65 years and older. Although flu vaccines are usually cheap and easily accessible, every year a large share of the population in the U.S. still does not get vaccinated. For example, during the 2022-2023 flu season, only about 35 percent of those aged 18 to 49 years received a flu vaccination.

In 2022, the leading causes of death for children aged one to four years in the United States were unintentional injuries and congenital malformations, deformations, and chromosomal abnormalities. At that time, around 31 percent of all deaths among these children were caused by unintentional injuries. Differences in causes of death among children by age Just as unintentional injuries are the leading cause of death among children aged one to four, it is also the leading cause of death for the age groups five to nine and 10 to 14. However, congenital malformations, deformations, and chromosomal abnormalities account for fewer deaths as children become older, while the share of deaths caused by cancer is higher among those aged five to nine and 10 to 14. In fact, cancer is the second leading cause of death among five to nine-year-olds, accounting for around 15 percent of all deaths. Sadly, the second leading cause of death among children aged 10 to 14 is intentional self-harm, with 13 percent of all deaths among those in this age group caused by suicide. Leading causes of death in the United States The leading causes of death in the United States are heart disease and malignant neoplasms. Together, these two diseases accounted for around 40 percent of all deaths in the United States in 2022. That year, COVID-19 was the fourth leading cause of death, with about six percent of all deaths caused by COVID-19. In 2022, the lifetime odds that the average person in the United States would die from heart disease was one in six, while the odds for cancer were one in seven and for COVID-19 one in 23.

ImportanceReligiosity has been associated with positive health outcomes. Hypothesized pathways for this association include religious practices, such as church attendance, that result in reduced stress.ObjectiveThe objective of this study was to examine the relationship between religiosity (church attendance), allostatic load (AL) (a physiologic measure of stress) and all-cause mortality in middle-aged adults.Design, setting and participantsData for this study are from NHANES III (1988–1994). The analytic sample (n = 5449) was restricted to adult participants, who were between 40–65 years of age at the time of interview, had values for at least 9 out of 10 clinical/biologic markers used to derive AL, and had complete information on church attendance.Main outcomes and measuresThe primary outcomes were AL and mortality. AL was derived from values for metabolic, cardiovascular, and nutritional/inflammatory clinical/biologic markers. Mortality was derived from a probabilistic algorithm matching the NHANES III Linked Mortality File to the National Death Index through December 31, 2006, providing up to 18 years follow-up. The primary predictor variable was baseline report of church attendance over the past 12 months. Cox proportional hazard logistic regression models contained key covariates including socioeconomic status, self-rated health, co-morbid medical conditions, social support, healthy eating, physical activity, and alcohol intake.ResultsChurchgoers (at least once a year) comprised 64.0% of the study cohort (n = 3782). Non-churchgoers had significantly higher overall mean AL scores and higher prevalence of high-risk values for 3 of the 10 markers of AL than did churchgoers. In bivariate analyses non-churchgoers, compared to churchgoers, had higher odds of an AL score 2–3 (OR 1.24; 95% CI 1.01, 1.50) or ≥4 (OR 1.38; 95% CI 1.11, 1.71) compared to AL score of 0–1. More frequent churchgoers (more than once a week) had a 55% reduction of all-cause mortality risk compared with non-churchgoers. (HR 0.45, CI 0.24–0.85) in the fully adjusted model that included AL.Conclusions and relevanceWe found a significant association between church attendance and mortality among middle-aged adults after full adjustments. AL, a measure of stress, only partially explained differences in mortality between church and non-church attendees. These findings suggest a potential independent effect of church attendance on mortality.

On September 20, 2017, Hurricane Maria made landfall in Puerto Rico, leaving widespread destruction in its path. The official death count for Puerto Rico after Hurricane Maria was 64 excess deaths, but that controversial death toll has been debated by a number of academic and independent researcher journalists. With the loss of electrical power and telecommunication systems for much of the island, it was unclear how many deaths in Puerto Rico were an immediate result of Hurricane Maria's destruction as opposed to the access to care conditions that prolonged. Santos-Burgoa et al. applied a time-series analysis of the Puerto Rico Vital Statistics data to estimate the death count over time. To consider how many people died as opposed to emigrated away from Puerto Rico, two counterfactual assumptions were used, a Census-based scenario and a Displacement-based scenario for expected population change. Under the Census scenario and the Displacement scenario, the estimated death counts in Puerto Rico was approximately 1200 deaths and 3000 deaths, respectively, where the Displacement scenario was acclaimed as the preferred model.

Due to copy-right issues, the article and supplementary materials should be accessed at the source website. Please use the following reference citation and doi to redirect there: Santos-Burgoa C, Sandberg J, Suárez E, Goldman-Hawes A, Zeger S, Garcia-Meza A, Pérez CM, Estrada-Merly N, Colón-Ramos U, Nazario CM, Andrade E. Differential and persistent risk of excess mortality from Hurricane Maria in Puerto Rico: a time-series analysis. The Lancet Planetary Health. 2018 Nov 1;2(11):e478-88. http://dx.doi.org/10.1016/S2542-5196(18)30209-2

Individual characteristics of deceased healthcare workers between March 2020 and July 2021 in Indonesia.

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.

Understanding the social determinants of Covid-19 infection and death is vital for effective Covid-19 early detection and mitigation strategies. This study aims to examine social determinants of Covid-19 infection and death in the context of rural Indonesia. We used Malang district government Covid-19 contact tracing data from 14,264 individuals, spanning the period from March 1, 2020 to July 29, 2020. The contact tracing data was merged with administrative data from 390 villages to determine whether village characteristics (i.e., the number of health workers, number of community-based healthcare interventions, access to Covid-19 referred hospitals, number of indigenous socio-cultural activities, poverty level and distance to a Covid-19 epicentre city) are associated with Covid-19 infection and death. We used multilevel logistic regression to take advantage of the nested structure of data at the village level. We found among the 14,264 samples, 551 individuals were confirmed infected with Covid-19, and 62 died of Covid-19. Individuals aged 18 and older, civil servants (non-health workers), and those having close contact with people with confirmed cases had a higher likelihood of infection with Covid-19. Greater numbers of community-based healthcare interventions and a lesser distance to a pandemic epicentre reduced the likelihood of infection with the virus. Males, older people, individuals with hypertension, individuals diagnosed with pneumonia, and those diagnosed with respiratory failure had a higher likelihood of death due to Covid-19. A greater number of community-based healthcare interventions seems to reduce the likelihood of Covid-19 infection, while better access to a Covid-19 referred hospital seems to reduce the risk of death among Covid-19 patients. The findings suggest the government to prioritise strategies to control the pandemic in rural area through empowering rural community in health education to prevent Covid-19 and in monitoring people mobility, while providing Covid-19 emergency services for rural areas for reducing mortality.