In the United States, the average person has a 1 in 6 chance of dying from heart disease and a 1 in 7 chance of dying from cancer. In comparison, the odds of dying from a dog attack are 1 in 44,499. Sadly, the odds of dying from an opioid overdose in the U.S. are 1 in 57, making death from an opioid overdose more likely than dying from a motor vehicle accident. Opioid overdose death rates have increased insignificantly in the U.S. over the past decade. Leading causes of death in the United States Given the high lifetime odds of dying from heart disease or cancer, it is unsurprising that heart disease and cancer are the leading causes of death in the United States. Together, heart disease and cancer account for around 42 percent of all deaths. Other leading causes of death include accidents, stroke, Alzheimer’s disease, and diabetes. However, in 2020 and 2021, COVID-19 was the third leading cause of death in the United States and remained the fourth leading cause of death in 2022, with around 44.5 deaths per 100,000 population. Heart disease in the U.S. In 2022, the death rate from heart disease in the United States was around 167 per 100,000 population. The states with the highest rates of death from heart disease at that time were Oklahoma, Mississippi, and Alabama. Coronary heart disease is the most common form of heart disease in the United States. Common risk factors for heart disease include high blood pressure, high cholesterol, smoking, excessive drinking, and being overweight or obese.

United States US: Probability of Dying at Age 20-24 Years: per 1000 data was reported at 5.100 Ratio in 2019. This records an increase from the previous number of 5.000 Ratio for 2018. United States US: Probability of Dying at Age 20-24 Years: per 1000 data is updated yearly, averaging 4.800 Ratio from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 5.500 Ratio in 1991 and a record low of 4.200 Ratio in 2013. United States US: Probability of Dying at Age 20-24 Years: per 1000 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Health Statistics. Probability of dying between age 20-24 years of age expressed per 1,000 youths age 20, 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.

Estimate of the probability of death for a single age group of men and women in the next 50 years or so.

Motor vehicle accidents were the most likely type of transportation to cause preventable deaths in the United States, with a person born in 2020 having a one in 101 chance of dying in a motor vehicle accident. At the other end of the scale is bus travel, where passengers have a one in 305,644 chance of being in a fatal accident.

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.

The following tables provide historical and projected probabilities of death by single year of age, sex, and year for the period 1900 through 2010. Death Probabilities for Males.

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

Poland PL: Probability of Dying at Age 20-24 Years: per 1000 data was reported at 2.800 Ratio in 2019. This stayed constant from the previous number of 2.800 Ratio for 2018. Poland PL: Probability of Dying at Age 20-24 Years: per 1000 data is updated yearly, averaging 3.500 Ratio from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 5.200 Ratio in 1990 and a record low of 2.800 Ratio in 2019. Poland PL: Probability of Dying at Age 20-24 Years: per 1000 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Poland – Table PL.World Bank.WDI: Health Statistics. Probability of dying between age 20-24 years of age expressed per 1,000 youths age 20, 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.

Singapore SG: Probability of Dying at Age 20-24 Years: per 1000 data was reported at 1.200 Ratio in 2019. This stayed constant from the previous number of 1.200 Ratio for 2018. Singapore SG: Probability of Dying at Age 20-24 Years: per 1000 data is updated yearly, averaging 2.250 Ratio from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 3.500 Ratio in 1990 and a record low of 1.200 Ratio in 2019. Singapore SG: Probability of Dying at Age 20-24 Years: per 1000 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Singapore – Table SG.World Bank.WDI: Health Statistics. Probability of dying between age 20-24 years of age expressed per 1,000 youths age 20, 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.

VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and Zip codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential Zip code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality. For the Zip code-level life expectancy calculation, it is assumed that postal Zip codes share the same boundaries as Zip Code Census Tabulation Areas (ZCTAs). More information on the relationship between Zip codes and ZCTAs can be found at https://www.census.gov/geo/reference/zctas.html. Zip code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 Zip code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for Zip codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest Zip code with population. Zip code population for 2000 estimates comes from the Decennial Census. Zip code population for 2013 estimates are from the American Community Survey (5-Year Average). The ACS provides Zip code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to Zip codes based on majority land-area.

Zip codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, Zip codes with populations of less than 5,000 were aggregated with neighboring Zip codes until the merged areas had a population of more than 5,000. In this way, the original 305 Bay Area Zip codes were reduced to 218 Zip code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

Odds ratios for the risk of dying from the coronavirus (COVID-19) by ethnicity in England and Wales.

In 2018 Niger was number 1 in Probability of Dying at Age 5-14 Years totalising 37.3 Units (Deaths) Per Thousand Children Aged 5, compared to 1 in 2017.

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.

The following tables provide historical and projected probabilities of death by age, sex, and year for the period 2011 - 2090. Death probabilities for females.

Probability of dying at age 5-14 years (per 1,000 children age 5) in Lebanon was reported at 1.7 in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. Lebanon - Probability of dying at age 5-14 years (per 1,000 children age 5) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Probability of dying at age 5-14 years (per 1,000 children age 5) in Japan was reported at 0.7 in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. Japan - Probability of dying at age 5-14 years (per 1,000 children age 5) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Australia Probability of Dying at Age 20-24 Years: per 1000 data was reported at 2.000 Ratio in 2019. This stayed constant from the previous number of 2.000 Ratio for 2018. Australia Probability of Dying at Age 20-24 Years: per 1000 data is updated yearly, averaging 2.850 Ratio from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 4.500 Ratio in 1990 and a record low of 2.000 Ratio in 2019. Australia Probability of Dying at Age 20-24 Years: per 1000 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Health Statistics. Probability of dying between age 20-24 years of age expressed per 1,000 youths age 20, 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.

Probability of dying at age 5-14 years (per 1,000 children age 5) in Pakistan was reported at 9.7 in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. Pakistan - Probability of dying at age 5-14 years (per 1,000 children age 5) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

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.

On May 10, 2021, Brazil ranked second in the world in COVID-19 deaths. Understanding risk factors, or social and ethnic inequality in health care according to a given city population and political or economic weakness is of paramount importance. Brazil had a seriousness COVID-19 outbreak in light of social and economic factors and its complex racial demographics. The objective of this study was to verify the odds of mortality of hospitalized patients during COVID-19 infection based on their economic, social, and epidemiological characteristics. We found that odds of death are greater among patients with comorbidities, neurological (1.99) and renal diseases (1.97), and immunodeficiency disorders (1.69). While the relative income (2.45) indicates that social factors have greater influence on mortality than the comorbidities studied. Patients living in the Northern macro-region of Brazil face greater chance of mortality compared to those in Central-South Brazil. We conclude that, during the studied period, the chances of mortality for COVID-19 in Brazil were more strongly influenced by socioeconomic poverty conditions than by natural comorbidities (neurological, renal, and immunodeficiency disorders), which were also very relevant. Regional factors are relevant in mortality rates given more individuals being vulnerable to poverty conditions.