The infant mortality rate is defined as the number of deaths of children under one year of age, expressed per 1 000 live births. Some of the international variation in infant mortality rates is due to variations among countries in registering practices for premature infants. The United States and Canada are two countries which register a much higher proportion of babies weighing less than 500g, with low odds of survival, resulting in higher reported infant mortality. In Europe, several countries apply a minimum gestational age of 22 weeks (or a birth weight threshold of 500g) for babies to be registered as live births. This indicator is measured in terms of deaths per 1 000 live births.

This indicator is a summary measure of premature mortality, providing an explicit way of weighting deaths occurring at younger ages, which may be preventable. The calculation of Potential Years of Life Lost (PYLL) involves summing up deaths occurring at each age and multiplying this with the number of remaining years to live up to a selected age limit (age 75 is used in OECD Health Statistics). In order to assure cross-country and trend comparison, the PYLL are standardised, for each country and each year. The total OECD population in 2010 is taken as the reference population for age standardisation. This indicator is presented as a total and per gender. It is measured in years lost per 100 000 inhabitants (total), per 100 000 men and per 100 000 women, aged 0-69.

Life expectancy at birth is defined as how long, on average, a newborn can expect to live, if current death rates do not change. However, the actual age-specific death rate of any particular birth cohort cannot be known in advance. If rates are falling, actual life spans will be higher than life expectancy calculated using current death rates. Life expectancy at birth is one of the most frequently used health status indicators. Gains in life expectancy at birth can be attributed to a number of factors, including rising living standards, improved lifestyle and better education, as well as greater access to quality health services. This indicator is presented as a total and per gender and is measured in years.

In 2022, the United States had a maternal mortality rate of **** per 100,000 live births, the highest number among selected high-income countries. Except for the U.S., all high-income countries have universal healthcare coverage that provides essential maternity services. In half of the selected high-income nations, there were fewer than **** maternal fatalities per 100,000 live births.

This statistic shows the 20 countries* with the highest infant mortality rate in 2024. An estimated 101.3 infants per 1,000 live births died in the first year of life in Afghanistan in 2024. Infant and child mortality Infant mortality usually refers to the death of children younger than one year. Child mortality, which is often used synonymously with infant mortality, is the death of children younger than five. Among the main causes are pneumonia, diarrhea – which causes dehydration – and infections in newborns, with malnutrition also posing a severe problem. As can be seen above, most countries with a high infant mortality rate are developing countries or emerging countries, most of which are located in Africa. Good health care and hygiene are crucial in reducing child mortality; among the countries with the lowest infant mortality rate are exclusively developed countries, whose inhabitants usually have access to clean water and comprehensive health care. Access to vaccinations, antibiotics and a balanced nutrition also help reducing child mortality in these regions. In some countries, infants are killed if they turn out to be of a certain gender. India, for example, is known as a country where a lot of girls are aborted or killed right after birth, as they are considered to be too expensive for poorer families, who traditionally have to pay a costly dowry on the girl’s wedding day. Interestingly, the global mortality rate among boys is higher than that for girls, which could be due to the fact that more male infants are actually born than female ones. Other theories include a stronger immune system in girls, or more premature births among boys.

One in every 100 children dies before completing one year of life. Around 68 percent of infant mortality is attributed to deaths of children before completing 1 month. 15,000 children die every day – Child mortality is an everyday tragedy of enormous scale that rarely makes the headlines Child mortality rates have declined in all world regions, but the world is not on track to reach the Sustainable Development Goal for child mortality Before the Modern Revolution child mortality was very high in all societies that we have knowledge of – a quarter of all children died in the first year of life, almost half died before reaching the end of puberty Over the last two centuries all countries in the world have made very rapid progress against child mortality. From 1800 to 1950 global mortality has halved from around 43% to 22.5%. Since 1950 the mortality rate has declined five-fold to 4.5% in 2015. All countries in the world have benefitted from this progress In the past it was very common for parents to see children die, because both, child mortality rates and fertility rates were very high. In Europe in the mid 18th century parents lost on average between 3 and 4 of their children Based on this overview we are asking where the world is today – where are children dying and what are they dying from?

5.4 million children died in 2017 – Where did these children die? Pneumonia is the most common cause of death, preterm births and neonatal disorders is second, and diarrheal diseases are third – What are children today dying from? This is the basis for answering the question what can we do to make further progress against child mortality? We will extend this entry over the course of 2020.

@article{owidchildmortality, author = {Max Roser, Hannah Ritchie and Bernadeta Dadonaite}, title = {Child and Infant Mortality}, journal = {Our World in Data}, year = {2013}, note = {https://ourworldindata.org/child-mortality} }

Notes: Persons of Hispanic origin may be of any race but are categorized as Hispanic for this analysis; other groups are non-Hispanic. Data were not available for Native Hawaiian or Pacific Islander people or American Indian Alaska Native People.

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.

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.

Notes: Persons of Hispanic origin may be of any race but are categorized as Hispanic for this analysis; other groups are non-Hispanic. AIAN refers to American Indian or Alaska Native. Data for Native Hawaiian or Pacific Islander infants were not available.

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

Graph and download economic data for Infant Mortality Rate for High Income Countries (SPDYNIMRTINHIC) from 1990 to 2023 about mortality, infant, income, and rate.

US: Mortality Rate: Under-5: per 1000 Live Births data was reported at 6.600 Ratio in 2017. This records a decrease from the previous number of 6.700 Ratio for 2016. US: Mortality Rate: Under-5: per 1000 Live Births data is updated yearly, averaging 11.750 Ratio from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 30.100 Ratio in 1960 and a record low of 6.600 Ratio in 2017. US: Mortality Rate: Under-5: 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 is the probability per 1,000 that a newborn baby will die before reaching age five, 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; 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.

US: Mortality Rate: Infant: per 1000 Live Births data was reported at 5.600 Ratio in 2016. This records a decrease from the previous number of 5.700 Ratio for 2015. US: Mortality Rate: Infant: per 1000 Live Births data is updated yearly, averaging 10.000 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 25.900 Ratio in 1960 and a record low of 5.600 Ratio in 2016. US: 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 USA – Table US.World Bank: 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.

US: Mortality Rate: Infant: Female: per 1000 Live Births data was reported at 5.000 Ratio in 2017. This records a decrease from the previous number of 5.200 Ratio for 2015. US: Mortality Rate: Infant: Female: per 1000 Live Births data is updated yearly, averaging 5.700 Ratio from Dec 1990 (Median) to 2017, with 5 observations. The data reached an all-time high of 8.300 Ratio in 1990 and a record low of 5.000 Ratio in 2017. US: Mortality Rate: Infant: 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 USA – Table US.World Bank: Health Statistics. Infant mortality rate, female is the number of female infants dying before reaching one year of age, per 1,000 female 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.

Maternal mortality ratio is defined as the number of female deaths due to obstetric causes (ICD-10 codes: A34, O00-O95, O98-O99) while pregnant or within 42 days of termination of pregnancy. The maternal mortality ratio indicates the likelihood of a pregnant person dying of obstetric causes. It is calculated by dividing the number of deaths among birthing people attributable to obstetric causes in a calendar year by the number of live births registered for the same period and is presented as a rate per 100,000 live births. The number of live births used in the denominator approximates the population of pregnant and birthing people who are at risk. Data are not presented for geographies with number of maternal deaths less than 11.Compared to other high-income countries, women in the US are more likely to die from childbirth or problems related to pregnancy. In addition, there are persistent disparities by race and ethnicity, with Black pregnant persons experiencing a much higher rate of maternal mortality compared to White pregnant persons. Improving the quality of medical care for pregnant individuals before, during, and after pregnancy can help reduce maternal deaths.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Moldova MD: Mortality Rate: Under-5: Female: per 1000 Live Births data was reported at 13.800 Ratio in 2017. This records a decrease from the previous number of 14.300 Ratio for 2015. Moldova MD: Mortality Rate: Under-5: Female: per 1000 Live Births data is updated yearly, averaging 15.300 Ratio from Dec 1990 (Median) to 2017, with 5 observations. The data reached an all-time high of 29.500 Ratio in 1990 and a record low of 13.800 Ratio in 2017. Moldova MD: 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 Moldova – Table MD.World Bank.WDI: 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.

Iraq IQ: Mortality Rate: Under-5: per 1000 Live Births data was reported at 30.400 Ratio in 2017. This records a decrease from the previous number of 31.300 Ratio for 2016. Iraq IQ: Mortality Rate: Under-5: per 1000 Live Births data is updated yearly, averaging 55.650 Ratio from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 191.600 Ratio in 1960 and a record low of 30.400 Ratio in 2017. Iraq IQ: Mortality Rate: Under-5: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Iraq – Table IQ.World Bank: Health Statistics. Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, 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; 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.

Dataset Description

This dataset serves as a comprehensive repository of global development metrics, consolidating data from multiple international organizations into a single, unified structure. It provides a granular view of the state of health, economy, and nutrition across 193 countries over a 30-year period (1990–2019).

The data is organized by Country, Year, and Gender (Male, Female, and Both Sexes), making it a valuable resource for longitudinal studies, demographic analysis, and socio-economic research. It combines high-level economic indicators (like GDP) with granular health metrics (specific mortality rates) and detailed nutritional breakdowns (diet composition by food group).

Content Overview

The dataset covers a wide spectrum of categories:

• Demographics & Economy: Population stats, GNI, GDP, and poverty rates.
• Mortality & Life Expectancy: Survival rates at various ages, maternal mortality, and life expectancy.
• Public Health: Incidence of infectious diseases (Malaria, Tuberculosis, Hepatitis B) and prevalence of health risks (Tobacco, road traffic accidents).
• Environmental Health: Mortality attributed to air pollution, sanitation access, and clean fuel availability.
• Nutrition: Detailed caloric and quantity breakdown of food consumption (fruits, vegetables, cereals, meats, etc.).
• Healthcare Infrastructure: Coverage of essential health services and density of medical professionals.

Sources

The data was extracted and unified via an ETL process from the following organizations:

• World Bank: World Development Indicators
• WHO: Global Health Observatory Data
• UNICEF: Data Warehouse
• Our World In Data: Statistics and Research
• FAOSTAT: Food and Agriculture Data
• Kaggle: WHO World Health Statistics 2020 Complete

Data Dictionary

Index Columns

• Country: Name of the country.
• Year: The calendar year of the recorded data.
• Gender: The gender category for the data (Female, Male, or Both sexes).

Demographics & Health Metrics

• Life Expectancy: The average number of years a newborn is expected to live.
• Infant Mortality Rate: Number of infants dying before reaching one year of age, per 1,000 live births.
  • Includes Low/High Confidence Interval (CI) columns.
• Under 5 Mortality Rate: Probability of a child dying before reaching age 5, per 1,000 live births.
  • Includes Low/High CI columns.
• Neonatal Mortality Rate: Number of deaths during the first 28 days of life per 1,000 live births.
  • Includes Low/High CI columns.
• Maternal Mortality Ratio: Number of maternal deaths due to childbirth per 100,000 live births.
  • Includes Low/High CI columns.
• Birth Rate: Number of births per 1,000 inhabitants.
• Death Rate: Number of deaths per 1,000 inhabitants.
• Adolescent Birth Rate: Number of births by women aged 15 to 19 per 1,000 women in that age range.
• % Population Aged 0-14 / 15-64 / 65+: Percentage of the total population falling into these specific age brackets.
• % Population Aged 65-69 / 70-74 / 75-79 / 80+: Granular breakdown of the elderly population percentages.
• Total Population: Total number of inhabitants.

Causes of Death & Disease

• % Death Cardiovascular: Probability of dying from cardiovascular diseases, cancer, diabetes, or chronic respiratory diseases between ages 30 and 70.
  • Includes Low/High CI columns.
• Incidence of Malaria: Number of malaria cases per 1,000 inhabitants at risk per year.
• Incidence of Tuberculosis: Estimated cases of tuberculosis per 100,000 inhabitants.
  • Includes Low/High CI columns.
• Hepatitis B Surface Antigen: Prevalence of hepatitis B surface antigen.
  • Includes Low/High CI columns.
• Road Traffic Deaths: Number of deaths due to traffic accidents per 100,000 people.
• Poisoning Mortality Rate: Deaths attributed to unintentional poisoning per 100,000 people.
• Conflict and Terrorism Deaths: Number of deaths due to armed conflicts and terrorism.
• Battle Related Deaths: Number of deaths related to battles in an armed conflict.
• % Injury Deaths: Percentage of deaths caused by injuries.
• Suicides Rate: Number of deliberate deaths per 100,000 inhabitants.
• Homicide Rate: Number of homicides per 100,000 inhabitants.

Air Pollution Mortality

• Air Pollution Death Rate Total: Probability of dying fr...

This compressed file is a snapshot of the entire Human Cause-of-death Data series as of January 8, 2026. For information about the HCD project (including a detailed methods protocol) and for the most up-to-date data series, please refer to the original HCD website at https://mortality.org/Data/HCD.

BackgroundWhile the high prevalence of preterm births and its impact on infant mortality in the US have been widely acknowledged, recent data suggest that even full-term births in the US face substantially higher mortality risks compared to European countries with low infant mortality rates. In this paper, we use the most recent birth records in the US to more closely analyze the primary causes underlying mortality rates among full-term births.Methods and findingsLinked birth and death records for the period 2010–2012 were used to identify the state- and cause-specific burden of infant mortality among full-term infants (born at 37–42 weeks of gestation). Multivariable logistic models were used to assess the extent to which state-level differences in full-term infant mortality (FTIM) were attributable to observed differences in maternal and birth characteristics. Random effects models were used to assess the relative contribution of state-level variation to FTIM. Hypothetical mortality outcomes were computed under the assumption that all states could achieve the survival rates of the best-performing states. A total of 10,175,481 infants born full-term in the US between January 1, 2010, and December 31, 2012, were analyzed. FTIM rate (FTIMR) was 2.2 per 1,000 live births overall, and ranged between 1.29 (Connecticut, 95% CI 1.08, 1.53) and 3.77 (Mississippi, 95% CI 3.39, 4.19) at the state level. Zero states reached the rates reported in the 6 low-mortality European countries analyzed (FTIMR < 1.25), and 13 states had FTIMR > 2.75. Sudden unexpected death in infancy (SUDI) accounted for 43% of FTIM; congenital malformations and perinatal conditions accounted for 31% and 11.3% of FTIM, respectively. The largest mortality differentials between states with good and states with poor FTIMR were found for SUDI, with particularly large risk differentials for deaths due to sudden infant death syndrome (SIDS) (odds ratio [OR] 2.52, 95% CI 1.86, 3.42) and suffocation (OR 4.40, 95% CI 3.71, 5.21). Even though these mortality differences were partially explained by state-level differences in maternal education, race, and maternal health, substantial state-level variation in infant mortality remained in fully adjusted models (SIDS OR 1.45, suffocation OR 2.92). The extent to which these state differentials are due to differential antenatal care standards as well as differential access to health services could not be determined due to data limitations. Overall, our estimates suggest that infant mortality could be reduced by 4,003 deaths (95% CI 2,284, 5,587) annually if all states were to achieve the mortality levels of the best-performing state in each cause-of-death category. Key limitations of the analysis are that information on termination rates at the state level was not available, and that causes of deaths may have been coded differentially across states.ConclusionsMore than 7,000 full-term infants die in the US each year. The results presented in this paper suggest that a substantial share of these deaths may be preventable. Potential improvements seem particularly large for SUDI, where very low rates have been achieved in a few states while average mortality rates remain high in most other areas. Given the high mortality burden due to SIDS and suffocation, policy efforts to promote compliance with recommended sleeping arrangements could be an effective first step in this direction.

One of the main tasks in road ecology is to identify hotspots of high mortality so that one can devise and implement mitigation measures. A common strategy to identify hotspots is to divide a road into several segments and determine when the number of collisions is above a given threshold, reflecting a desired significance level obtained assuming a probability distribution (often the Poisson). The problem of this approach when applied to each segment individually is that the probability of identifying false hotspots is very high, i.e., the probability of making a type I error is very high. For instance, if we establish the threshold based as the top of a 95% confidence interval, then one should expect to incorrectly identify just by chance five false hotspots in every 100 segments. Although one may argue that such overly cautionary approach may be beneficial from a biological conservation perspective, it may lead to the waste of resources and, probably worse, it may raise doubts on the methodology adopted and the credibility of those suggesting it. The problem of multiple comparison occurs in several scientific areas and several corrections have been suggested. Here, we apply three different approaches to the identification of hotspots: a method similar to that of the Bonferroni correction; the false discovery rate (FDR); and a Bayesian approach that consists of a hierarchical Poisson model. The Bonferroni approach reduces the probability of type I errors, yet the probability of type II errors (rejecting a true hotspot), is very high and thus this procedure has low power. FDR method increases the power of the test while keeping the probability of identifying false hotspots low. The Bayesian approach uses the information obtained from all segments to infer the probability of a segment being a hotspotand avoids some of the problems inherent to the two previous approaches. We discuss the application of these three methods and give recommendations to the identification of hotspots with a view to providing a wide range of practitioners’ procedures that are reliable and simple to use in real situations.