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.

As of 2023, the countries with the highest death rates worldwide were Monaco, Bulgaria, and Latvia. In these countries, there were ** to ** deaths per 1,000 people. The country with the lowest death rate is Qatar, where there is just *** death per 1,000 people. Leading causes of death The leading causes of death worldwide are, by far, cardiovascular diseases, accounting for ** percent of all deaths in 2021. That year, there were **** million deaths worldwide from ischaemic heart disease and **** million from stroke. Interestingly, a worldwide survey from that year found that people greatly underestimate the proportion of deaths caused by cardiovascular disease, but overestimate the proportion of deaths caused by suicide, interpersonal violence, and substance use disorders. Death in the United States In 2023, there were around **** million deaths in the United States. The leading causes of death in the United States are currently heart disease and cancer, accounting for a combined ** percent of all deaths in 2023. Lung and bronchus cancer is the deadliest form of cancer worldwide, as well as in the United States. In the U.S. this form of cancer is predicted to cause around ****** deaths among men alone in the year 2025. Prostate cancer is the second-deadliest cancer for men in the U.S. while breast cancer is the second deadliest for women. In 2023, the tenth leading cause of death in the United States was COVID-19. Deaths due to COVID-19 resulted in a significant rise in the total number of deaths in the U.S. in 2020 and 2021 compared to 2019, and it was the third leading cause of death in the U.S. during those years.

The leading causes of death in Massachusetts are cancer, heart disease, unintentional injury, stroke, and chronic lower respiratory disease. These mortality rates tend to be higher for people of color; and Black residents have a higher premature mortality rate overall and Asian residents have a higher rate of mortality due to stroke.

Premature death rate measures mortality by counting deaths at earlier ages more than deaths at later ages. For example, when a person dies at 20, this death contributes 55 years of potential life lost. In contrast, when a person dies at age 70, this death contributes only five years of potential life lost to a county. For our purposes, premature deaths occur before age 75. Counties with older populations are more likely to have higher crude premature death rates than counties with younger populations. Therefore, when age-adjusted, we remove the effect of differently aged populations as a risk factor for premature death. This allows us to make a fair comparison of premature death rates across counties.

Infant Death Rate - This indicator shows the infant mortality rate per 1,000 live births. Infant mortality has long been considered the most sensitive indicator of the overall health of a population. While there have been several decades of improvement in infant mortality, Maryland’s rate remains higher than the national average. Link to Data Details

As of March 10, 2023, the death rate from COVID-19 in the state of New York was 397 per 100,000 people. New York is one of the states with the highest number of COVID-19 cases.

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.

Canada CA: Mortality Rate: Under-5: Female: per 1000 Live Births data was reported at 4.700 Ratio in 2023. This stayed constant from the previous number of 4.700 Ratio for 2022. Canada CA: Mortality Rate: Under-5: Female: per 1000 Live Births data is updated yearly, averaging 7.000 Ratio from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 28.600 Ratio in 1960 and a record low of 4.700 Ratio in 2023. Canada CA: Mortality Rate: Under-5: Female: per 1000 Live Births 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: Social: Health Statistics. Under-five mortality rate, female is the probability per 1,000 that a newborn female baby will die before reaching age five, if subject to female 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;Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys. 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. This is a sex-disaggregated indicator for Sustainable Development Goal 3.2.1 [https://unstats.un.org/sdgs/metadata/].

Infant Death Rate - This indicator shows the infant mortality rate per 1,000 live births. Infant mortality has long been considered the most sensitive indicator of the overall health of a population. While there have been several decades of improvement in infant mortality, Maryland’s rate remains higher than the national average.

Chad TD: Mortality Rate: Infant: per 1000 Live Births data was reported at 58.700 Ratio in 2023. This records a decrease from the previous number of 60.300 Ratio for 2022. Chad TD: Mortality Rate: Infant: per 1000 Live Births data is updated yearly, averaging 114.000 Ratio from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 142.000 Ratio in 1960 and a record low of 58.700 Ratio in 2023. Chad TD: Mortality Rate: Infant: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chad – Table TD.World Bank.WDI: Social: Health Statistics. Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given 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;Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys. 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.

The death rate, also known as the mortality rate, measures the frequency of deaths within a specific population over a defined period, typically expressed as the number of deaths per 1,000 or 100,000 individuals annually. It is a fundamental demographic indicator that helps assess population health, socioeconomic conditions, and the effectiveness of public health interventions. Various factors influence the death rate, including access to healthcare, nutrition, socioeconomic status, environmental quality, and lifestyle habits. Developed nations often report lower death rates due to advanced healthcare systems, higher standards of living, and effective disease prevention programs. Conversely, developing countries may face higher death rates due to limited healthcare access, infectious diseases, malnutrition, and lower living standards. By analyzing death rate trends, researchers can identify health disparities, evaluate the impact of policy changes, and guide interventions to improve life expectancy and quality of life across different populations.

ABSTRACT Background : The Covid-19 pandemic associated with the SARS-CoV-2 has caused very high death tolls in many countries, while it has had less prevalence in other countries of Africa and Asia. Climate and geographic conditions, as well as other epidemiologic and demographic conditions, were a matter of debate on whether or not they could have an effect on the prevalence of Covid-19. Objective : In the present work, we sought a possible relevance of the geographic location of a given country on its Covid-19 prevalence. On the other hand, we sought a possible relation between the history of epidemiologic and demographic conditions of the populations and the prevalence of Covid-19 across four continents (America, Europe, Africa, and Asia). We also searched for a possible impact of pre-pandemic alcohol consumption in each country on the two year death tolls across the four continents. Methods : We have sought the death toll caused by Covid-19 in 39 countries and obtained the registered deaths from specialized web pages. For every country in the study, we have analysed the correlation of the Covid-19 death numbers with its geographic latitude, and its associated climate conditions, such as the mean annual temperature, the average annual sunshine hours, and the average annual UV index. We also analyzed the correlation of the Covid-19 death numbers with epidemiologic conditions such as cancer score and Alzheimer score, and with demographic parameters such as birth rate, mortality rate, fertility rate, and the percentage of people aged 65 and above. In regard to consumption habits, we searched for a possible relation between alcohol intake levels per capita and the Covid-19 death numbers in each country. Correlation factors and determination factors, as well as analyses by simple linear regression and polynomial regression, were calculated or obtained by Microsoft Exell software (2016). Results : In the present study, higher numbers of deaths related to Covid-19 pandemic were registered in many countries in Europe and America compared to other countries in Africa and Asia. The analysis by polynomial regression generated an inverted bell-shaped curve and a significant correlation between the Covid-19 death numbers and the geographic latitude of each country in our study. Higher death numbers were registered in the higher geographic latitudes of both hemispheres, while lower scores of deaths were registered in countries located around the equator line. In a bell shaped curve, the latitude levels were negatively correlated to the average annual levels (last 10 years) of temperatures, sunshine hours, and UV index of each country, with the highest scores of each climate parameter being registered around the equator line, while lower levels of temperature, sunshine hours, and UV index were registered in higher latitude countries. In addition, the linear regression analysis showed that the Covid-19 death numbers registered in the 39 countries of our study were negatively correlated with the three climate factors of our study, with the temperature as the main negatively correlated factor with Covid-19 deaths. On the other hand, cancer and Alzheimer's disease scores, as well as advanced age and alcohol intake, were positively correlated to Covid-19 deaths, and inverted bell-shaped curves were obtained when expressing the above parameters against a country’s latitude. Instead, the (birth rate/mortality rate) ratio and fertility rate were negatively correlated to Covid-19 deaths, and their values gave bell-shaped curves when expressed against a country’s latitude. Conclusion : The results of the present study prove that the climate parameters and history of epidemiologic and demographic conditions as well as nutrition habits are very correlated with Covid-19 prevalence. The results of the present study prove that low levels of temperature, sunshine hours, and UV index, as well as negative epidemiologic and demographic conditions and high scores of alcohol intake may worsen Covid-19 prevalence in many countries of the northern hemisphere, and this phenomenon could explain their high Covid-19 death tolls. Keywords : Covid-19, Coronavirus, SARS-CoV-2, climate, temperature, sunshine hours, UV index, cancer, Alzheimer disease, alcohol.

Live births by usual residence of mother, and General Fertility Rates (GFR), and Deaths and Standardised Mortality Ratio (SMR) by ward and local authority.

The births and deaths data comes from ONS Vital Statistics Table 4.
Small area data is only available directly from ONS under licence.

The general fertility rate (GFR) is the number of live births per 1,000 women aged 15-44.

SMR measures whether the population of an area has a higher or lower number of deaths than expected based on the age profile of the population (more deaths are expected in older populations). The SMR is defined as follows: SMR = (Observed no. of deaths per year)/(Expected no. of deaths per year).

Rates are provisional, they are based on the GLA 2011 based SHLAA ward projections (standard) released in January 2012. At national level, however, they are based on the mid-year population estimates.

More information is on the ONS website.

Belarus BY: Mortality Rate: Infant: per 1000 Live Births data was reported at 1.900 Ratio in 2023. This records a decrease from the previous number of 2.000 Ratio for 2022. Belarus BY: Mortality Rate: Infant: per 1000 Live Births data is updated yearly, averaging 12.350 Ratio from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 36.900 Ratio in 1960 and a record low of 1.900 Ratio in 2023. Belarus BY: Mortality Rate: Infant: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Belarus – Table BY.World Bank.WDI: Social: Health Statistics. Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given 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;Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys. 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.

Death rate has been age-adjusted by the 2000 U.S. standard populaton. All-cause mortality is an important measure of community health. All-cause mortality is heavily driven by the social determinants of health, with significant inequities observed by race and ethnicity and socioeconomic status. Black residents have consistently experienced the highest all-cause mortality rate compared to other racial and ethnic groups. During the COVID-19 pandemic, Latino residents also experienced a sharp increase in their all-cause mortality rate compared to White residents, demonstrating a reversal in the previously observed mortality advantage, in which Latino individuals historically had higher life expectancy and lower mortality than White individuals despite having lower socioeconomic status on average. The disproportionately high all-cause mortality rates observed among Black and Latino residents, especially since the onset of the COVID-19 pandemic, are due to differences in social and economic conditions and opportunities that unfairly place these groups at higher risk of developing and dying from a wide range of health conditions, including COVID-19.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

BR: Mortality Rate: Under-5: Male: per 1000 Live Births data was reported at 16.000 Ratio in 2023. This records a decrease from the previous number of 16.200 Ratio for 2022. BR: Mortality Rate: Under-5: Male: per 1000 Live Births data is updated yearly, averaging 64.300 Ratio from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 182.300 Ratio in 1960 and a record low of 16.000 Ratio in 2023. BR: Mortality Rate: Under-5: Male: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Social: Health Statistics. Under-five mortality rate, male is the probability per 1,000 that a newborn male baby will die before reaching age five, if subject to male 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;Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys. 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. This is a sex-disaggregated indicator for Sustainable Development Goal 3.2.1 [https://unstats.un.org/sdgs/metadata/].

Russia has consistently had the highest crude death rate of any of the BRICS countries since 2000. However, this is not because Russia has the lowest living standards in the bloc, but rather the opposite. When compared with the other BRICS countries Russia has the highest level of human development with oldest population and the lowest birth rates; this results in very low population growth, and death rates per capita are much higher. Additionally, widespread alcohol and substance abuse, alongside unhealthy lifestyles, did increase Russia's death rate in the *****, particularly among men. South Africa South Africa, on the other hand, has the lowest level of demographic development, which is the reason it has the second highest death rate. In the early ***** especially, death rates rose due to the rapid spread of HIV/AIDS in the south of the continent, although living standards have improved significantly, and the death rate has dropped as a result.

For the other three countries, death rates have been much more consistent since 2000, due to their progression through the demographic transition.

United States US: Mortality Rate: Under-5: Male: per 1000 Live Births data was reported at 7.200 Ratio in 2017. This records a decrease from the previous number of 7.400 Ratio for 2015. United States US: Mortality Rate: Under-5: Male: per 1000 Live Births data is updated yearly, averaging 8.000 Ratio from Dec 1990 (Median) to 2017, with 5 observations. The data reached an all-time high of 12.500 Ratio in 1990 and a record low of 7.200 Ratio in 2017. United States US: Mortality Rate: Under-5: Male: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Under-five mortality rate, male is the probability per 1,000 that a newborn male baby will die before reaching age five, if subject to male 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; Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.

This publication of the SHMI relates to discharges in the reporting period October 2023 - September 2024. The SHMI is the ratio between the actual number of patients who die following hospitalisation at the trust and the number that would be expected to die on the basis of average England figures, given the characteristics of the patients treated there. The SHMI covers patients admitted to hospitals in England who died either while in hospital or within 30 days of being discharged. To help users of the data understand the SHMI, trusts have been categorised into bandings indicating whether a trust's SHMI is 'higher than expected', 'as expected' or 'lower than expected'. For any given number of expected deaths, a range of observed deaths is considered to be 'as expected'. If the observed number of deaths falls outside of this range, the trust in question is considered to have a higher or lower SHMI than expected. The expected number of deaths is a statistical construct and is not a count of patients. The difference between the number of observed deaths and the number of expected deaths cannot be interpreted as the number of avoidable deaths or excess deaths for the trust. The SHMI is not a measure of quality of care. A higher than expected number of deaths should not immediately be interpreted as indicating poor performance and instead should be viewed as a 'smoke alarm' which requires further investigation. Similarly, an 'as expected' or 'lower than expected' SHMI should not immediately be interpreted as indicating satisfactory or good performance. Trusts may be located at multiple sites and may be responsible for 1 or more hospitals. A breakdown of the data by site of treatment is also provided, as well as a breakdown of the data by diagnosis group. Further background information and supporting documents, including information on how to interpret the SHMI, are available on the SHMI homepage (see Related Links).

Luxembourg LU: Mortality Rate: Infant: Male: per 1000 Live Births data was reported at 2.100 Ratio in 2016. This records a decrease from the previous number of 2.200 Ratio for 2015. Luxembourg LU: Mortality Rate: Infant: Male: per 1000 Live Births data is updated yearly, averaging 2.500 Ratio from Dec 1990 (Median) to 2016, with 5 observations. The data reached an all-time high of 8.100 Ratio in 1990 and a record low of 2.100 Ratio in 2016. Luxembourg LU: Mortality Rate: Infant: Male: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Luxembourg – Table LU.World Bank: Health Statistics. Infant mortality rate, male is the number of male infants dying before reaching one year of age, per 1,000 male live births in a given 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; Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.