The child mortality rate in the United States, for children under the age of five, was 462.9 deaths per thousand births in 1800. This means that for every thousand babies born in 1800, over 46 percent did not make it to their fifth birthday. Over the course of the next 220 years, this number has dropped drastically, and the rate has dropped to its lowest point ever in 2020 where it is just seven deaths per thousand births. Although the child mortality rate has decreased greatly over this 220 year period, there were two occasions where it increased; in the 1870s, as a result of the fourth cholera pandemic, smallpox outbreaks, and yellow fever, and in the late 1910s, due to the Spanish Flu pandemic.

For most of the world, throughout most of human history, the average life expectancy from birth was around 24. This figure fluctuated greatly depending on the time or region, and was higher than 24 in most individual years, but factors such as pandemics, famines, and conflicts caused regular spikes in mortality and reduced life expectancy. Child mortality The most significant difference between historical mortality rates and modern figures is that child and infant mortality was so high in pre-industrial times; before the introduction of vaccination, water treatment, and other medical knowledge or technologies, women would have around seven children throughout their lifetime, but around half of these would not make it to adulthood. Accurate, historical figures for infant mortality are difficult to ascertain, as it was so prevalent, it took place in the home, and was rarely recorded in censuses; however, figures from this source suggest that the rate was around 300 deaths per 1,000 live births in some years, meaning that almost one in three infants did not make it to their first birthday in certain periods. For those who survived to adolescence, they could expect to live into their forties or fifties on average. Modern figures It was not until the eradication of plague and improvements in housing and infrastructure in recent centuries where life expectancy began to rise in some parts of Europe, before industrialization and medical advances led to the onset of the demographic transition across the world. Today, global life expectancy from birth is roughly three times higher than in pre-industrial times, at almost 73 years. It is higher still in more demographically and economically developed countries; life expectancy is over 82 years in the three European countries shown, and over 84 in Japan. For the least developed countries, mostly found in Sub-Saharan Africa, life expectancy from birth can be as low as 53 years.

Over the past 160 years, life expectancy (from birth) in the United States has risen from 39.4 years in 1860, to 78.9 years in 2020. One of the major reasons for the overall increase of life expectancy in the last two centuries is the fact that the infant and child mortality rates have decreased by so much during this time. Medical advancements, fewer wars and improved living standards also mean that people are living longer than they did in previous centuries.

Despite this overall increase, the life expectancy dropped three times since 1860; from 1865 to 1870 during the American Civil War, from 1915 to 1920 during the First World War and following Spanish Flu epidemic, and it has dropped again between 2015 and now. The reason for the most recent drop in life expectancy is not a result of any specific event, but has been attributed to negative societal trends, such as unbalanced diets and sedentary lifestyles, high medical costs, and increasing rates of suicide and drug use.

This table includes key figures on mortality in the Dutch population broken down by gender. The figures include totals and ratios of deceased persons, infant mortality, mortality in babies younger than 4 weeks and perinatal mortality (after a gestation period of 24 weeks or more and after a gestation period of 28 weeks or more). The table also presents figures on life expectancy at birth and average age at death. For additional information on Mortality the reader is referred to the Dutch tables. Data available from: 1950 Status of the figures: All data recorded in this publication are final data. The 2023 figures on stillbirths and (multiple) births are provisional, the other figures in the table are final. Changes as of 9 December 2024: The provisional figures on the number of live births and stillbirths do not include children who were born at a gestational age that is unknown. These cases were included in the final figures for previous years. However, the 2023 data shows a larger number of children born at an unknown gestational age than in previous years. Based on an internal analysis for 2022, it appears that in the majority of cases involving an unknown gestational age, the child was born at less than 24 weeks. To ensure that the provisional 2023 figures do not overestimate the number of stillborn children born at a gestational age of over 24 weeks, children born at an unknown gestational age have now been excluded. When will new figures be published? Final 2023 figures on the number of stillbirths and the number of births are expected to be added to the table in de third quarter of 2025. In the third quarter of 2025 final figures of 2024 will be published in this publication.

Background: Mortality rate rapidly decreases with age after birth, and, simultaneously, the spectrum of death causes show remarkable changes with age. This study analyzed age-associated decreases in mortality rate from diseases of all main chapters of the 10th revision of the International Classification of Diseases.Methods: The number of deaths was extracted from the mortality database of the World Health Organization. As zero cases could be ascertained for a specific age category, the Halley method was used to calculate the mortality rates in all possible calendar years and in all countries combined.Results: All causes mortality from the 1st day of life to the age of 10 years can be represented by an inverse proportion model with a single parameter. High coefficients of determination were observed for total mortality in all populations (arithmetic mean = 0.9942 and standard deviation = 0.0039).Slower or no mortality decrease with age was detected in the 1st year of life, while the inverse proportion method was valid for the age range [1, 10) years in most of all main chapters with three exceptions. The decrease was faster for the chapter “Certain conditions originating in the perinatal period” (XVI).The inverse proportion was valid already from the 1st day for the chapter “Congenital malformations, deformations and chromosomal abnormalities” (XVII).The shape of the mortality decrease was very different for the chapter “Neoplasms” (II) and the rates of mortality from neoplasms were age-independent in the age range [1, 10) years in all populations.Conclusion: The theory of congenital individual risks of death is presented and can explain the results. If it is valid, latent congenital impairments may be present among all cases of death that are not related to congenital impairments. All results are based on published data, and the data are presented as a supplement.

Norway NO: Mortality Rate: Under-5: per 1000 Live Births data was reported at 2.600 Ratio in 2017. This stayed constant from the previous number of 2.600 Ratio for 2016. Norway NO: Mortality Rate: Under-5: per 1000 Live Births data is updated yearly, averaging 9.550 Ratio from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 22.600 Ratio in 1960 and a record low of 2.600 Ratio in 2017. Norway NO: 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 Norway – Table NO.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.

BackgroundDespite the sharp decline in global under-5 deaths since 1990, uneven progress has been achieved across and within countries. In sub-Saharan Africa (SSA), the Millennium Development Goals (MDGs) for child mortality were met only by a few countries. Valid concerns exist as to whether the region would meet new Sustainable Development Goals (SDGs) for under-5 mortality. We therefore examine further sources of variation by assessing age patterns, trends, and forecasts of mortality rates.Methods and findingsData came from 106 nationally representative Demographic and Health Surveys (DHSs) with full birth histories from 31 SSA countries from 1990 to 2017 (a total of 524 country-years of data). We assessed the distribution of age at death through the following new demographic analyses. First, we used a direct method and full birth histories to estimate under-5 mortality rates (U5MRs) on a monthly basis. Second, we smoothed raw estimates of death rates by age and time by using a two-dimensional P-Spline approach. Third, a variant of the Lee–Carter (LC) model, designed for populations with limited data, was used to fit and forecast age profiles of mortality. We used mortality estimates from the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) to adjust, validate, and minimize the risk of bias in survival, truncation, and recall in mortality estimation. Our mortality model revealed substantive declines of death rates at every age in most countries but with notable differences in the age patterns over time. U5MRs declined from 3.3% (annual rate of reduction [ARR] 0.1%) in Lesotho to 76.4% (ARR 5.2%) in Malawi, and the pace of decline was faster on average (ARR 3.2%) than that observed for infant (IMRs) (ARR 2.7%) and neonatal (NMRs) (ARR 2.0%) mortality rates. We predict that 5 countries (Kenya, Rwanda, Senegal, Tanzania, and Uganda) are on track to achieve the under-5 sustainable development target by 2030 (25 deaths per 1,000 live births), but only Rwanda and Tanzania would meet both the neonatal (12 deaths per 1,000 live births) and under-5 targets simultaneously. Our predicted NMRs and U5MRs were in line with those estimated by the UN IGME by 2030 and 2050 (they overlapped in 27/31 countries for NMRs and 22 for U5MRs) and by the Institute for Health Metrics and Evaluation (IHME) by 2030 (26/31 and 23/31, respectively). This study has a number of limitations, including poor data quality issues that reflected bias in the report of births and deaths, preventing reliable estimates and predictions from a few countries.ConclusionsTo our knowledge, this study is the first to combine full birth histories and mortality estimates from external reliable sources to model age patterns of under-5 mortality across time in SSA. We demonstrate that countries with a rapid pace of mortality reduction (ARR ≥ 3.2%) across ages would be more likely to achieve the SDG mortality targets. However, the lower pace of neonatal mortality reduction would prevent most countries from achieving those targets: 2 countries would reach them by 2030, 13 between 2030 and 2050, and 13 after 2050.

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

This table includes key figures on mortality in the Dutch population broken down by gender. The figures include totals and ratios of deceased persons, infant mortality, mortality in babies younger than 4 weeks and perinatal mortality (after a gestation period of 24 weeks or more and after a gestation period of 28 weeks or more). The table also presents figures on life expectancy at birth and average age at death.

For additional information on Mortality the reader is referred to the Dutch tables.

Data available from: 1950

Status of the figures: All data recorded in this publication are final data. The 2023 figures on stillbirths and (multiple) births are provisional, the other figures in the table are final.

Changes as of 9 December 2024: The provisional figures on the number of live births and stillbirths do not include children who were born at a gestational age that is unknown. These cases were included in the final figures for previous years. However, the 2023 data shows a larger number of children born at an unknown gestational age than in previous years. Based on an internal analysis for 2022, it appears that in the majority of cases involving an unknown gestational age, the child was born at less than 24 weeks. To ensure that the provisional 2023 figures do not overestimate the number of stillborn children born at a gestational age of over 24 weeks, children born at an unknown gestational age have now been excluded.

When will new figures be published? Final 2023 figures on the number of stillbirths and the number of births are expected to be added to the table in de third quarter of 2025. In the third quarter of 2025 final figures of 2024 will be published in this publication.

Number of infant deaths and infant mortality rates, by age group (neonatal and post-neonatal), 1991 to most recent year.

Footnotes: 1 Sources: Statistics Canada, Canadian Vital Statistics, Birth, Death and Stillbirth Databases. The table 13-10-0110-01 is an update of table 13-10-0408-01. 2 Infant mortality corresponds to the death of a child under one year of age. Expressed as a rate per 1,000 live births. 3 Perinatal deaths include late fetal deaths (stillbirths with a gestational age of 28 weeks or more) and early neonatal deaths (deaths of infants aged less than one week). 4 Numbers and rates in this table may differ from those found in similar data published by the Vital Statistics program as the data here have been tabulated based on postal codes available for place of residence. 5 2017 data for Yukon are not available. 6 The number of births, stillbirths, and deaths in Ontario for 2016 and 2017 are considered preliminary. 7 Due to improvements in methodology and timeliness, the duration of data collection has been shortened compared to previous years. As a result, there may have been fewer births and stillbirths captured by the time of the release. The 2017 data are therefore considered preliminary. 8 A census metropolitan area (CMA) is an area consisting of one or more adjacent municipalities situated around a major urban core. To form a census metropolitan area, the urban core must have a population of at least 100,000. The CMAs are those defined for the 2016 Census. To form a census agglomeration, the urban core must have a population of at least 10,000. 9 The metropolitan influenced zone (MIZ) classification is an approach to better differentiate areas of Canada outside of census metropolitan areas and census agglomerations. Census subdivisions that lie outside these areas are classified into one of four zones of influence. They are assigned to categories based on the flow of residents travelling to work in an urban area with a population greater than 10,000. Municipalities where more that 30% of the residents commute to work in an urban core are assigned to the strong MIZ category. Municipalities where between 5% and 30% of the residents commute to work in an urban core are assigned to the moderate MIZ category. Municipalities where between 0% and 5% of the residents commute to work in an urban core are assigned to the weak MIZ category. Municipalities where fewer than 40 or none of the residents commute to work in an urban core are assigned to the zero MIZ category. 10 Geographical areas are modified every 5 years to reflect the most recent census definitions, therefore, data are not strictly comparable historically. 11 Counts and rates in this table are based on three consecutive years of data. 12 The 95% confidence interval (CI) illustrates the degree of variability associated with a rate. 13 Wide confidence intervals (CIs) indicate high variability, thus, these rates should be interpreted and compared with due caution. 14 The following standard symbols are used in this Statistics Canada table: (..) for figures not available for a specific reference period, (...) for figures not applicable and (x) for figures suppressed to meet the confidentiality requirements of the Statistics Act. 15 The figures shown in the tables have been subjected to a confidentiality procedure known as controlled rounding to prevent the possibility of associating statistical data with any identifiable individual. Under this method, all figures, including totals and margins, are rounded either up or down to a multiple of 5. Controlled rounding has the advantage over other types of rounding of producing additive tables as well as offering more protection.

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.

Footnotes: 1 Sources: Statistics Canada, Canadian Vital Statistics, Birth, Death and Stillbirth Databases. The table 13-10-0110-01 is an update of table 13-10-0408-01. 2 Infant mortality corresponds to the death of a child under one year of age. Expressed as a rate per 1,000 live births. 3 Perinatal deaths include late fetal deaths (stillbirths with a gestational age of 28 weeks or more) and early neonatal deaths (deaths of infants aged less than one week). 4 Numbers and rates in this table may differ from those found in similar data published by the Vital Statistics program as the data here have been tabulated based on postal codes available for place of residence. 5 2017 data for Yukon are not available. 6 The number of births, stillbirths, and deaths in Ontario for 2016 and 2017 are considered preliminary. 7 Due to improvements in methodology and timeliness, the duration of data collection has been shortened compared to previous years. As a result, there may have been fewer births and stillbirths captured by the time of the release. The 2017 data are therefore considered preliminary. 8 A census metropolitan area (CMA) is an area consisting of one or more adjacent municipalities situated around a major urban core. To form a census metropolitan area, the urban core must have a population of at least 100,000. The CMAs are those defined for the 2016 Census. To form a census agglomeration, the urban core must have a population of at least 10,000. 9 The metropolitan influenced zone (MIZ) classification is an approach to better differentiate areas of Canada outside of census metropolitan areas and census agglomerations. Census subdivisions that lie outside these areas are classified into one of four zones of influence. They are assigned to categories based on the flow of residents travelling to work in an urban area with a population greater than 10,000. Municipalities where more that 30% of the residents commute to work in an urban core are assigned to the strong MIZ category. Municipalities where between 5% and 30% of the residents commute to work in an urban core are assigned to the moderate MIZ category. Municipalities where between 0% and 5% of the residents commute to work in an urban core are assigned to the weak MIZ category. Municipalities where fewer than 40 or none of the residents commute to work in an urban core are assigned to the zero MIZ category. 10 Geographical areas are modified every 5 years to reflect the most recent census definitions, therefore, data are not strictly comparable historically. 11 Counts and rates in this table are based on three consecutive years of data. 12 The 95% confidence interval (CI) illustrates the degree of variability associated with a rate. 13 Wide confidence intervals (CIs) indicate high variability, thus, these rates should be interpreted and compared with due caution. 14 The following standard symbols are used in this Statistics Canada table: (..) for figures not available for a specific reference period, (...) for figures not applicable and (x) for figures suppressed to meet the confidentiality requirements of the Statistics Act. 15 The figures shown in the tables have been subjected to a confidentiality procedure known as controlled rounding to prevent the possibility of associating statistical data with any identifiable individual. Under this method, all figures, including totals and margins, are rounded either up or down to a multiple of 5. Controlled rounding has the advantage over other types of rounding of producing additive tables as well as offering more protection.

Footnotes: 1 Sources: Statistics Canada, Canadian Vital Statistics, Birth, Death and Stillbirth Databases. The table 13-10-0110-01 is an update of table 13-10-0408-01. 2 Infant mortality corresponds to the death of a child under one year of age. Expressed as a rate per 1,000 live births. 3 Perinatal deaths include late fetal deaths (stillbirths with a gestational age of 28 weeks or more) and early neonatal deaths (deaths of infants aged less than one week). 4 Numbers and rates in this table may differ from those found in similar data published by the Vital Statistics program as the data here have been tabulated based on postal codes available for place of residence. 5 2017 data for Yukon are not available. 6 The number of births, stillbirths, and deaths in Ontario for 2016 and 2017 are considered preliminary. 7 Due to improvements in methodology and timeliness, the duration of data collection has been shortened compared to previous years. As a result, there may have been fewer births and stillbirths captured by the time of the release. The 2017 data are therefore considered preliminary. 8 A census metropolitan area (CMA) is an area consisting of one or more adjacent municipalities situated around a major urban core. To form a census metropolitan area, the urban core must have a population of at least 100,000. The CMAs are those defined for the 2016 Census. To form a census agglomeration, the urban core must have a population of at least 10,000. 9 The metropolitan influenced zone (MIZ) classification is an approach to better differentiate areas of Canada outside of census metropolitan areas and census agglomerations. Census subdivisions that lie outside these areas are classified into one of four zones of influence. They are assigned to categories based on the flow of residents travelling to work in an urban area with a population greater than 10,000. Municipalities where more that 30% of the residents commute to work in an urban core are assigned to the strong MIZ category. Municipalities where between 5% and 30% of the residents commute to work in an urban core are assigned to the moderate MIZ category. Municipalities where between 0% and 5% of the residents commute to work in an urban core are assigned to the weak MIZ category. Municipalities where fewer than 40 or none of the residents commute to work in an urban core are assigned to the zero MIZ category. 10 Geographical areas are modified every 5 years to reflect the most recent census definitions, therefore, data are not strictly comparable historically. 11 Counts and rates in this table are based on three consecutive years of data. 12 The 95% confidence interval (CI) illustrates the degree of variability associated with a rate. 13 Wide confidence intervals (CIs) indicate high variability, thus, these rates should be interpreted and compared with due caution. 14 The following standard symbols are used in this Statistics Canada table: (..) for figures not available for a specific reference period, (...) for figures not applicable and (x) for figures suppressed to meet the confidentiality requirements of the Statistics Act. 15 The figures shown in the tables have been subjected to a confidentiality procedure known as controlled rounding to prevent the possibility of associating statistical data with any identifiable individual. Under this method, all figures, including totals and margins, are rounded either up or down to a multiple of 5. Controlled rounding has the advantage over other types of rounding of producing additive tables as well as offering more protection.

Germany DE: Mortality Rate: Under-5: per 1000 Live Births data was reported at 3.700 Ratio in 2023. This stayed constant from the previous number of 3.700 Ratio for 2022. Germany DE: Mortality Rate: Under-5: per 1000 Live Births data is updated yearly, averaging 6.350 Ratio from Dec 1968 (Median) to 2023, with 56 observations. The data reached an all-time high of 27.100 Ratio in 1968 and a record low of 3.700 Ratio in 2023. Germany DE: 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 Germany – Table DE.World Bank.WDI: Social: 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. 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 the Sustainable Development Goal indicator 3.2.1[https://unstats.un.org/sdgs/metadata/].

BackgroundThere has been increasing interest in measuring under-five mortality as a health indicator and as a critical measure of human development. In countries with complete vital registration systems that capture all births and deaths, under-five mortality can be directly calculated. In the absence of a complete vital registration system, however, child mortality must be estimated using surveys that ask women to report the births and deaths of their children. Two survey methods exist for capturing this information: summary birth histories and complete birth histories. A summary birth history requires a minimum of only two questions: how many live births has each mother had and how many of them have survived. Indirect methods are then applied using the information from these two questions and the age of the mother to estimate under-five mortality going back in time prior to the survey. Estimates generated from complete birth histories are viewed as the most accurate when surveys are required to estimate under-five mortality, especially for the most recent time periods. However, it is much more costly and labor intensive to collect these detailed data, especially for the purpose of generating small area estimates. As a result, there is a demand for improvement of the methods employing summary birth history data to produce more accurate as well as subnational estimates of child mortality.Methods and FindingsWe used data from 166 Demographic and Health Surveys (DHS) to develop new empirically based methods of estimating under-five mortality using children ever born and children dead data. We then validated them using both in- and out-of-sample analyses. We developed a range of methods on the basis of three dimensions of the problem: (1) approximating the average length of exposure to mortality from a mother's set of children using either maternal age or time since first birth; (2) using cohort and period measures of the fraction of children ever born that are dead; and (3) capturing country and regional variation in the age pattern of fertility and mortality. We focused on improving estimates in the most recent time periods prior to a survey where the traditional indirect methods fail. In addition, all of our methods incorporated uncertainty. Validated against under-five estimates generated from complete birth histories, our methods outperformed the standard indirect method by an average of 43.7% (95% confidence interval [CI] 41.2–45.2). In the 5 y prior to the survey, the new methods resulted in a 53.3% (95% CI 51.3–55.2) improvement. To illustrate the value of this method for local area estimation, we applied our new methods to an analysis of summary birth histories in the 1990, 2000, and 2005 Mexican censuses, generating subnational estimates of under-five mortality for each of 233 jurisdictions.ConclusionsThe new methods significantly improve the estimation of under-five mortality using summary birth history data. In areas without vital registration data, summary birth histories can provide accurate estimates of child mortality. Because only two questions are required of a female respondent to generate these data, they can easily be included in existing survey programs as well as routine censuses of the population. With the wider application of these methods to census data, countries now have the means to generate estimates for subnational areas and population subgroups, important for measuring and addressing health inequalities and developing local policy to improve child survival.Please see later in the article for the Editors' Summary

Footnotes: 1 Sources: Statistics Canada, Canadian Vital Statistics, Birth, Death and Stillbirth Databases. The table 13-10-0110-01 is an update of table 13-10-0408-01. 2 Infant mortality corresponds to the death of a child under one year of age. Expressed as a rate per 1,000 live births. 3 Perinatal deaths include late fetal deaths (stillbirths with a gestational age of 28 weeks or more) and early neonatal deaths (deaths of infants aged less than one week). 4 Numbers and rates in this table may differ from those found in similar data published by the Vital Statistics program as the data here have been tabulated based on postal codes available for place of residence. 5 2017 data for Yukon are not available. 6 The number of births, stillbirths, and deaths in Ontario for 2016 and 2017 are considered preliminary. 7 Due to improvements in methodology and timeliness, the duration of data collection has been shortened compared to previous years. As a result, there may have been fewer births and stillbirths captured by the time of the release. The 2017 data are therefore considered preliminary. 8 A census metropolitan area (CMA) is an area consisting of one or more adjacent municipalities situated around a major urban core. To form a census metropolitan area, the urban core must have a population of at least 100,000. The CMAs are those defined for the 2016 Census. To form a census agglomeration, the urban core must have a population of at least 10,000. 9 The metropolitan influenced zone (MIZ) classification is an approach to better differentiate areas of Canada outside of census metropolitan areas and census agglomerations. Census subdivisions that lie outside these areas are classified into one of four zones of influence. They are assigned to categories based on the flow of residents travelling to work in an urban area with a population greater than 10,000. Municipalities where more that 30% of the residents commute to work in an urban core are assigned to the strong MIZ category. Municipalities where between 5% and 30% of the residents commute to work in an urban core are assigned to the moderate MIZ category. Municipalities where between 0% and 5% of the residents commute to work in an urban core are assigned to the weak MIZ category. Municipalities where fewer than 40 or none of the residents commute to work in an urban core are assigned to the zero MIZ category. 10 Geographical areas are modified every 5 years to reflect the most recent census definitions, therefore, data are not strictly comparable historically. 11 Counts and rates in this table are based on three consecutive years of data. 12 The 95% confidence interval (CI) illustrates the degree of variability associated with a rate. 13 Wide confidence intervals (CIs) indicate high variability, thus, these rates should be interpreted and compared with due caution. 14 The following standard symbols are used in this Statistics Canada table: (..) for figures not available for a specific reference period, (...) for figures not applicable and (x) for figures suppressed to meet the confidentiality requirements of the Statistics Act. 15 The figures shown in the tables have been subjected to a confidentiality procedure known as controlled rounding to prevent the possibility of associating statistical data with any identifiable individual. Under this method, all figures, including totals and margins, are rounded either up or down to a multiple of 5. Controlled rounding has the advantage over other types of rounding of producing additive tables as well as offering more protection.

Abstract

The second National Family Health Survey (NFHS-2), conducted in 1998-99, provides information on fertility, mortality, family planning, and important aspects of nutrition, health, and health care. The International Institute for Population Sciences (IIPS) coordinated the survey, which collected information from a nationally representative sample of more than 90,000 ever-married women age 15-49. The NFHS-2 sample covers 99 percent of India's population living in all 26 states. This report is based on the survey data for 25 of the 26 states, however, since data collection in Tripura was delayed due to local problems in the state.

IIPS also coordinated the first National Family Health Survey (NFHS-1) in 1992-93. Most of the types of information collected in NFHS-2 were also collected in the earlier survey, making it possible to identify trends over the intervening period of six and one-half years. In addition, the NFHS-2 questionnaire covered a number of new or expanded topics with important policy implications, such as reproductive health, women's autonomy, domestic violence, women's nutrition, anaemia, and salt iodization.

The NFHS-2 survey was carried out in two phases. Ten states were surveyed in the first phase which began in November 1998 and the remaining states (except Tripura) were surveyed in the second phase which began in March 1999. The field staff collected information from 91,196 households in these 25 states and interviewed 89,199 eligible women in these households. In addition, the survey collected information on 32,393 children born in the three years preceding the survey. One health investigator on each survey team measured the height and weight of eligible women and children and took blood samples to assess the prevalence of anaemia.

SUMMARY OF FINDINGS

POPULATION CHARACTERISTICS

Three-quarters (73 percent) of the population lives in rural areas. The age distribution is typical of populations that have recently experienced a fertility decline, with relatively low proportions in the younger and older age groups. Thirty-six percent of the population is below age 15, and 5 percent is age 65 and above. The sex ratio is 957 females for every 1,000 males in rural areas but only 928 females for every 1,000 males in urban areas, suggesting that more men than women have migrated to urban areas.

The survey provides a variety of demographic and socioeconomic background information. In the country as a whole, 82 percent of household heads are Hindu, 12 percent are Muslim, 3 percent are Christian, and 2 percent are Sikh. Muslims live disproportionately in urban areas, where they comprise 15 percent of household heads. Nineteen percent of household heads belong to scheduled castes, 9 percent belong to scheduled tribes, and 32 percent belong to other backward classes (OBCs). Two-fifths of household heads do not belong to any of these groups.

Questions about housing conditions and the standard of living of households indicate some improvements since the time of NFHS-1. Sixty percent of households in India now have electricity and 39 percent have piped drinking water compared with 51 percent and 33 percent, respectively, at the time of NFHS-1. Sixty-four percent of households have no toilet facility compared with 70 percent at the time of NFHS-1.

About three-fourths (75 percent) of males and half (51 percent) of females age six and above are literate, an increase of 6-8 percentage points from literacy rates at the time of NFHS-1. The percentage of illiterate males varies from 6-7 percent in Mizoram and Kerala to 37 percent in Bihar and the percentage of illiterate females varies from 11 percent in Mizoram and 15 percent in Kerala to 65 percent in Bihar. Seventy-nine percent of children age 6-14 are attending school, up from 68 percent in NFHS-1. The proportion of children attending school has increased for all ages, particularly for girls, but girls continue to lag behind boys in school attendance. Moreover, the disparity in school attendance by sex grows with increasing age of children. At age 6-10, 85 percent of boys attend school compared with 78 percent of girls. By age 15-17, 58 percent of boys attend school compared with 40 percent of girls. The percentage of girls 6-17 attending school varies from 51 percent in Bihar and 56 percent in Rajasthan to over 90 percent in Himachal Pradesh and Kerala.

Women in India tend to marry at an early age. Thirty-four percent of women age 15-19 are already married including 4 percent who are married but gauna has yet to be performed. These proportions are even higher in the rural areas. Older women are more likely than younger women to have married at an early age: 39 percent of women currently age 45-49 married before age 15 compared with 14 percent of women currently age 15-19. Although this indicates that the proportion of women who marry young is declining rapidly, half the women even in the age group 20-24 have married before reaching the legal minimum age of 18 years. On average, women are five years younger than the men they marry. The median age at marriage varies from about 15 years in Madhya Pradesh, Bihar, Uttar Pradesh, Rajasthan, and Andhra Pradesh to 23 years in Goa.

As part of an increasing emphasis on gender issues, NFHS-2 asked women about their participation in household decisionmaking. In India, 91 percent of women are involved in decision-making on at least one of four selected topics. A much lower proportion (52 percent), however, are involved in making decisions about their own health care. There are large variations among states in India with regard to women's involvement in household decisionmaking. More than three out of four women are involved in decisions about their own health care in Himachal Pradesh, Meghalaya, and Punjab compared with about two out of five or less in Madhya Pradesh, Orissa, and Rajasthan. Thirty-nine percent of women do work other than housework, and more than two-thirds of these women work for cash. Only 41 percent of women who earn cash can decide independently how to spend the money that they earn. Forty-three percent of working women report that their earnings constitute at least half of total family earnings, including 18 percent who report that the family is entirely dependent on their earnings. Women's work-participation rates vary from 9 percent in Punjab and 13 percent in Haryana to 60-70 percent in Manipur, Nagaland, and Arunachal Pradesh.

FERTILITY AND FAMILY PLANNING

Fertility continues to decline in India. At current fertility levels, women will have an average of 2.9 children each throughout their childbearing years. The total fertility rate (TFR) is down from 3.4 children per woman at the time of NFHS-1, but is still well above the replacement level of just over two children per woman. There are large variations in fertility among the states in India. Goa and Kerala have attained below replacement level fertility and Karnataka, Himachal Pradesh, Tamil Nadu, and Punjab are at or close to replacement level fertility. By contrast, fertility is 3.3 or more children per woman in Meghalaya, Uttar Pradesh, Rajasthan, Nagaland, Bihar, and Madhya Pradesh. More than one-third to less than half of all births in these latter states are fourth or higher-order births compared with 7-9 percent of births in Kerala, Goa, and Tamil Nadu.

Efforts to encourage the trend towards lower fertility might usefully focus on groups within the population that have higher fertility than average. In India, rural women and women from scheduled tribes and scheduled castes have somewhat higher fertility than other women, but fertility is particularly high for illiterate women, poor women, and Muslim women. Another striking feature is the high level of childbearing among young women. More than half of women age 20-49 had their first birth before reaching age 20, and women age 15-19 account for almost one-fifth of total fertility. Studies in India and elsewhere have shown that health and mortality risks increase when women give birth at such young ages?both for the women themselves and for their children. Family planning programmes focusing on women in this age group could make a significant impact on maternal and child health and help to reduce fertility.

INFANT AND CHILD MORTALITY

NFHS-2 provides estimates of infant and child mortality and examines factors associated with the survival of young children. During the five years preceding the survey, the infant mortality rate was 68 deaths at age 0-11 months per 1,000 live births, substantially lower than 79 per 1,000 in the five years preceding the NFHS-1 survey. The child mortality rate, 29 deaths at age 1-4 years per 1,000 children reaching age one, also declined from the corresponding rate of 33 per 1,000 in NFHS-1. Ninety-five children out of 1,000 born do not live to age five years. Expressed differently, 1 in 15 children die in the first year of life, and 1 in 11 die before reaching age five. Child-survival programmes might usefully focus on specific groups of children with particularly high infant and child mortality rates, such as children who live in rural areas, children whose mothers are illiterate, children belonging to scheduled castes or scheduled tribes, and children from poor households. Infant mortality rates are more than two and one-half times as high for women who did not receive any of the recommended types of maternity related medical care than for mothers who did receive all recommended types of care.

HEALTH, HEALTH CARE, AND NUTRITION

Promotion of maternal and child health has been one of the most important components of the Family Welfare Programme of the Government of India. One goal is for each pregnant woman to receive at least three antenatal check-ups plus two tetanus toxoid injections and a full course of iron and folic acid supplementation. In India, mothers of 65 percent of the children

The significant increase in life expectancy over the past 75 years has largely been driven by reductions in infant and child mortality, and has seen life expectancy from birth increase by 27 years between 1950 and 2024. However, this is not the only driver of increased life expectancy, as humanity has also got much better at prolonging life for adults. In 1950, 65-year-olds could expect to live for another 11 years on average, while this has risen to almost 18 years in 2024. The notable dips in life expectancy are due to China's Great Leap Forward around 1960, famine and conflict in Asia (especially Bangladesh) around 1970, and the COVID-19 pandemic in the early 2020s.

Iraq: Deaths of children five to fourteen years of age per 1000 live births: The latest value from 2022 is 2 deaths per 1000 births, a decline from 3 deaths per 1000 births in 2021. In comparison, the world average is 3 deaths per 1000 births, based on data from 187 countries. Historically, the average for Iraq from 1990 to 2022 is 4 deaths per 1000 births. The minimum value, 2 deaths per 1000 births, was reached in 2022 while the maximum of 6 deaths per 1000 births was recorded in 1990.