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.

The Global Subnational Infant Mortality Rates, Version 2.01 consist of Infant Mortality Rate (IMR) estimates for 234 countries and territories, 143 of which include subnational Units. The data are benchmarked to the year 2015 (Version 1 was benchmarked to the year 2000), and are drawn from national offices, Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other sources from 2006 to 2014. In addition to Infant Mortality Rates, Version 2.01 includes crude estimates of births and infant deaths, which could be aggregated or disaggregated to different geographies to calculate infant mortality rates at different scales or resolutions, where births are the rate denominator and infant deaths are the rate numerator. Boundary inputs are derived primarily from the Gridded Population of the World, Version 4 (GPWv4) data collection. National and subnational data are mapped to grid cells at a spatial resolution of 30 arc-seconds (~1 km) (Version 1 has a spatial resolution of 1/4 degree, ~28 km at the equator), allowing for easy integration with demographic, environmental, and other spatial data.

Years of life lost due to mortality from all circulatory diseases (ICD-10 I00-I99). Years of life lost (YLL) is a measure of premature mortality. Its primary purpose is to compare the relative importance of different causes of premature death within a particular population and it can therefore be used by health planners to define priorities for the prevention of such deaths. It can also be used to compare the premature mortality experience of different populations for a particular cause of death. The concept of years of life lost is to estimate the length of time a person would have lived had they not died prematurely. By inherently including the age at which the death occurs, rather than just the fact of its occurrence, the calculation is an attempt to better quantify the burden, or impact, on society from the specified cause of mortality. Legacy unique identifier: P00520

In 2023, with just *** death per one thousand people, Qatar and the United Arab Emirates were the countries with the lowest death rates worldwide. This statistic shows a ranking of the 20 countries with the lowest death rates worldwide, as of 2023. Health in high-income countries Countries with the highest life expectancies are also often high-income countries with well-developed economic, social and health care systems, providing adequate resources and access to treatment for health concerns. Health care expenditure as a share of GDP varies per country; for example, spending in the United States is higher than in other OECD countries due to higher costs and prices for care services and products. In developed countries, the main burden of disease is often due to non-communicable diseases occurring in old age, such as cardiovascular diseases and cancer. High burden in low-income countries The countries with the lowest life expectancy worldwide are all in Africa- including Nigeria, Chad, and Lesotho- with life expectancies reaching up to 20 years shorter than the average global life expectancy. Leading causes of death in low-income countries include respiratory infections and diarrheal diseases, as these countries are often hit with the double burden of infectious diseases plus non-communicable diseases, such as those related to cardiovascular pathologies. Additionally, these countries often lack the resources and infrastructure to sustain effective healthcare systems and fail to provide appropriate access and treatment for their populations.

Excel workbook of age-standardised baseline mortality rates (BMRs) for each US county by race and ethnicity used for calculating racial-ethnic disparities in health burdens for air pollution from the major oil and gas lifecycle stages in the United States.The workbook includes 3 sheets:BMRs for all-cause mortality in 25+ years population for calculating premature mortality from exposure to fine particular matter (PM2.5).BMRs for all-cause mortality in 65+ years population for calculating premature mortality from exposure to nitrogen dioxide (NO2), andBMRs for all-ages chronic obstructive pulmonary disease (COPD) mortality from exposure to ozone air pollution.Raw BMRs from the US US Centers for Disease Control and Prevention Wide-ranging ONline Data for Epidemiologic Research (CDC WONDER) are processed to gap fill data not reported at the county level. This data gap filling is detailed in Vohra et al. (2025) Science Advances, "The health burden and racial-ethnic disparities of air pollution from the major oil and gas lifecycle stages in the United States", doi:10.1126/sciadv.adu2241.

COVID-19 rate of death, or the known deaths divided by confirmed cases, was over ten percent in Yemen, the only country that has 1,000 or more cases. This according to a calculation that combines coronavirus stats on both deaths and registered cases for 221 different countries. Note that death rates are not the same as the chance of dying from an infection or the number of deaths based on an at-risk population. By April 26, 2022, the virus had infected over 510.2 million people worldwide, and led to a loss of 6.2 million. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. Note that Statista aims to also provide domestic source material for a more complete picture, and not to just look at one particular source. Examples are these statistics on the confirmed coronavirus cases in Russia or the COVID-19 cases in Italy, both of which are from domestic sources. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

A word on the flaws of numbers like this

People are right to ask whether these numbers are at all representative or not for several reasons. First, countries worldwide decide differently on who gets tested for the virus, meaning that comparing case numbers or death rates could to some extent be misleading. Germany, for example, started testing relatively early once the country’s first case was confirmed in Bavaria in January 2020, whereas Italy tests for the coronavirus postmortem. Second, not all people go to see (or can see, due to testing capacity) a doctor when they have mild symptoms. Countries like Norway and the Netherlands, for example, recommend people with non-severe symptoms to just stay at home. This means not all cases are known all the time, which could significantly alter the death rate as it is presented here. Third and finally, numbers like this change very frequently depending on how the pandemic spreads or the national healthcare capacity. It is therefore recommended to look at other (freely accessible) content that dives more into specifics, such as the coronavirus testing capacity in India or the number of hospital beds in the UK. Only with additional pieces of information can you get the full picture, something that this statistic in its current state simply cannot provide.

This data shows premature deaths (Age under 75), numbers and rates by gender, as 3-year moving-averages.

All-Cause Mortality rates are a summary indicator of population health status. All-cause mortality is related to Life Expectancy, and both may be influenced by health inequalities.

Directly Age-Standardised Rates (DASR) are shown in the data (where numbers are sufficient) so that death rates can be directly compared between areas. The DASR calculation applies Age-specific rates to a Standard (European) population to cancel out possible effects on crude rates due to different age structures among populations, thus enabling direct comparisons of rates.

A limitation on using mortalities as a proxy for prevalence of health conditions is that mortalities may give an incomplete view of health conditions in an area, as ill-health might not lead to premature death.

Data source: Office for Health Improvement and Disparities (OHID), Public Health Outcomes Framework (PHOF) indicator ID 108. This data is updated annually.

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.

Statbel, the Belgian statistical office, publishes an overview of the provisional mortality figures for each year, for all causes of death. The publication contains provisional mortality figures for all Belgian municipalities, and an analysis of the mortality by age category, gender and by month.

General mortality statistics are compiled on the basis of data from the National Register of Natural Persons (RNPP). They make it possible to consolidate the statistics on causes of death, the source of which is the civil status forms. This statistic breaks down the deaths of people residing in Belgium according to sex, municipality of residence (district, province and region), month of death, civil status and nationality (Belgian or foreign). They also make it possible to calculate the gross mortality rate, i.e. the ratio between the number of deaths during the year and the population in the middle of that year.

Among nations of the UK, Northern Ireland had the highest number of live births per 1,000 in 2021, at 11.6, followed by England at 10.5, Wales at 9.3, and Scotland at 8.7. The crude birth rate has fallen for all nations of the UK when compared with 1971, while Northern Ireland has consistently had the highest number of live births per 1,000 people. Long-term birth trends After reaching a postwar peak of 18.8 births per 1,000 people, the UK's crude birth rate has declined considerably, falling to a low of just eleven births per 1,000 people in 2020. In that year, there were just 681,560 live births, compared with over one million in 1964. Additionally, the average age of mothers in the UK has been steadily increasing since the mid-1970s. In 1975, for example, the average age at which mothers gave birth was 26.4 years, compared with 30.9 in 2021. Millennials overtake Boomers as the largest generation Due to the large number of births that happened in the years following the Second World War, the generation born during this time were called Baby Boomers, and until 2020 were the largest generation in the UK. Since that year, the Millennial generation, born between 1981 and 1996, has been the largest generational cohort. In 2023, there were almost 14.7 million Millennials, just over 14 million Generation Xers (born between 1965 and 1980), and around 13.6 million Baby Boomers. Generation Z, the generation immediately after Millennials, numbered approximately 13.2 million in this year.

Abstract

The JPFHS is part of the worldwide Demographic and Health Surveys (DHS) program, which is designed to collect data on fertility, family planning, and maternal and child health.

The 1990 Jordan Population and Family Health Survey (JPFHS) was carried out as part of the Demographic and Health Survey (DHS) program. The Demographic and Health Surveys is assisting governments and private agencies in the implementation of household surveys in developing countries.

The JPFIS was designed to provide information on levels and trends of fertility, infant and child mortality, and family planning. The survey also gathered information on breastfeeding, matemal and child health cam, the nutritional status of children under five, as well as the characteristics of households and household members.

The main objectives of the project include: a) Providing decision makers with a data base and analyses useful for informed policy choices, b) Expanding the international population and health data base, c) Advancing survey methodology, and d) Developing skills and resources necessary to conduct high quality demographic and health surveys in the participating countries.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men

Kind of data

Sample survey data

Sampling procedure

The sample for the JPFHS survey was selected to be representative of the major geographical regions, as well as the nation as a whole. The survey adopted a stratified, multi-stage sampling design. In each governorate, localities were classified into 9 strata according to the estimated population size in 1989. The sampling design also allowed for the survey results to be presented according to major cities (Amman, Irbid and Zarqa), other urban localities, and the rural areas. Localities with fewer than 5,000 people were considered rural.

For this survey, 349 sample units were drawn, containing 10,708 housing units for the individual interview. Since the survey used a separate household questionnaire, the Department of Statistics doubled the household sample size and added a few questions on labor force, while keeping the original individual sample intact. This yielded 21,172 housing units. During fieldwork for the household interview, it was found that 4,359 household units were ineligible either because the dwelling was vacant or destroyed, the household was absent during the team visit, or some other reason. There were 16,296 completed household interviews out of 16,813 eligible households, producing a response rate of 96.9 percent.

The completed household interviews yielded 7,246 women eligible for the individual interview, of which 6,461 were successfully interviewed, producing a response rate of 89.2 percent.

Note: See detailed description of sample design in APPENDIX A of the survey report.

Mode of data collection

Face-to-face

Research instrument

The 1990 JPFIS utilized two questionnaires, one for the household interview and the other for individual women. Both questionnaires were developed first in English and then translated into Arabic. The household questionnaire was used to list all members of the sample households, including usual residents as well as visitors. For each member of the household, basic demographic and socioeconomic characteristics were recorded and women eligible for the individual interview were identified. To be eligible for individual interview, a woman had to be a usual member of the household (part of the de jure population), ever-married, and between 15 and 49 years of age. The household questionnaire was expanded from the standard DHS-II model questionnaire to facilitate the estimation of adult mortality using the orphanhood and widowhood techniques. In addition, the questionnaire obtained information on polygamy, economic activity of persons 15 years of age and over, family type, type of insurance covering the household members, country of work in the summer of 1990 which coincided with the Gulf crisis, and basic data for the calculation of the crude birth rate and the crude death rate. Additional questions were asked about deceased women if they were ever-married and age 15-49, in order to obtain information for the calculation of materoal mortality indices.

The individual questionnaire is a modified version of the standard DHS-II model "A" questionnaire. Experience gained from previous surveys, in particular the 1983 Jordan Fertility and Family Health Survey, and the questionnaire developed by the Pan Arab Project for Child Development (PAPCHILD), were useful in the discussions on the content of the JPFHS questionnaire. A major change from the DHS-II model questionnaire was the rearrangement of the sections so that the marriage section came before reproduction; this allowed the interview to flow more smoothly. Questions on children's cause of death based on verbal autopsy were added to the section on health, which, due to its size, was split into two parts. The first part focused on antenatal care and breastfeeding; the second part examined measures for prevention of childhood diseases and information on the morbidity and mortality of children loom since January 1985. As questions on sexual relations were considered too sensitive, they were replaced by questions about the husband's presence in the household during the specified time period; this served as a proxy for recent sexual activity.

The JPFHS individual questionnaire consists of nine sections: - Respondent's background and household characteristics - Marriage - Reproduction - Contraception - Breastfeeding and health - Immunization, morbidity, and child mortality - Fertility preferences - Husband's background, residence, and woman's work - Height and weight of children

Response rate

For the individual interview, the number of eligible women found in the selected households and the number of women successfully interviewed are presented. The data indicate a high response rate for the household interview (96.9 percent), and a lower rate for the individual interview (89.2 percent). Women in large cities have a slightly lower response rate (88.6 percent) than those in other areas. Most of the non-response for the individual interview was due to the absence of respondents and the postponement of interviews which were incomplete.

Note: See summarized response rates by place of residence in Table 1.1 of the survey report.

Sampling error estimates

The results from sample surveys are affected by two types of errors, non-sampling error and sampling error. Nonsampling error is due to mistakes made in carrying out field activities, such as failure to locate and interview the correct household, errors in the way the questions are asked, misunderstanding on the part of either the interviewer or the respondent, data entry errors, etc. Although efforts were made during the design and implementation of the JPFHS to minimize this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically

Sampling errors, on the other hand, can be measured statistically. The sample of women selected in the JPFHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each one would have yielded results that differed somewhat from the actual sample selected. The sampling error is a measure of the variability between all possible samples; although it is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of standard error of a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which one can reasonably assured that, apart from nonsampling errors, the true value of the variable for the whole population falls. For example, for any given statistic calculated from a sample survey, the value of that same statistic as measured in 95 percent of all possible samples with the same design (and expected size) will fall within a range of plus or minus two times the standard error of that statistic.

If the sample of women had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the JPFI-IS sample design depended on stratification, stages and clusters. Consequently, it was necessary to utilize more complex formulas. The computer package CLUSTERS, developed by the International Statistical Institute for the World Fertility Survey, was used to assist in computing the sampling errors with the proper statistical methodology.

Note: See detailed estimate of sampling error calculation in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting - Births by calendar year since birth - Reporting of age at death in days - Reporting of age at death in months

Note: See detailed tables in APPENDIX C of the report which is presented in this documentation.

6.90 (per thousand population) in 2021. Birth Rate (or Crude Birth Rate) refers to the ratio of the number of births to the average population (or mid-period population) during a certain period of time (usually a year), expressed in ‰. Birth rate refers to annual birth rate. The following formula is used: (Number of births)/(Annual average population)*1000‰. Number of births in the formula refers to live births, i.e. when a baby has breathed or showed any vital phenomena regardless of the length of pregnancy. Annual average population is the average of the number of population at the beginning of the year and that at the end of the year. Sometimes it is substituted by the mid-year population.

13,72 (per thousand population) in 2019. Birth Rate (or Crude Birth Rate) refers to the ratio of the number of births to the average population (or mid-period population) during a certain period of time (usually a year), expressed in ‰. Birth rate refers to annual birth rate. The following formula is used: (Number of births)/(Annual average population)*1000‰. Number of births in the formula refers to live births, i.e. when a baby has breathed or showed any vital phenomena regardless of the length of pregnancy. Annual average population is the average of the number of population at the beginning of the year and that at the end of the year. Sometimes it is substituted by the mid-year population.

Abstract

The importance of administrative statistics is much worth as it offers a good opportunity to get data at a cheaper cost compared to censuses and sample surveys. Administrative statistics are also very essential to calculate some important demographic measures for instance health administrative statistics such as crude birth rate, general fertility rate, age specific fertility rate, total fertility rate, gross reproduction rate, net reproduction rate, crude death rates, marriage and divorce rates, etc., under the condition that they are complete and accurate.

Geographic coverage

National coverage

Analysis unit

The development of Health Administrative Data Progress Assessment focused on women and children as units of analysis.

Universe

This assessment targeted women and children

Kind of data

Administrative records data [adm]

Mode of data collection

Other [oth]

Cleaning operations

For the calculation of fertility indicators, two sources of administrative statistics( HMIS and CRVS) were used.

The Health Management Information System (HMIS) has collected the aggregated number of births in 2015 and 2016. For the corresponding years, the Civil Registration and Vital Statistics system (CRVS) has collected the number of births by the age of their mothers at the time of birth. To calculate fertility indicators like ASFR, TFR, and GRR, we need the number of births tabulated according to age of their mothers at birth.

Since the number of births registered in HMIS is close to expectation vis-à-vis the expected annual birth, fertility indicators were computed using HMIS data and these data have been imputed following the births distribution by age of the mothers (15-49) from the CRVS assuming that the same distribution of births according to the age of their mothers applies.

Data appraisal

A combination of sources of data namely Health Management Information System (HMIS) and Civil Registration and Vital Statistics web based application (CRVS) is very useful for quality data. The 4th Rwanda Population and Housing Census conducted in 2012 and Rwanda Demographic and Health Survey (RDHS) conducted in 2014/15 were also used to benchmark on expectations and achievements for now.

In this study, the combination of length, growth rate, mortality rate and also the current of status Operation Carp and roach commercially as important species in southern waters were reviewed. Samples were taken from beach sine net catch and fish were offered at fish market. The age composition of carp and roach was 1 to 16 and 1 to 4 years respectively, most catch carp and roach was in length range of 31 to 39 cm and 18 to 20 cm respectively. Growth parameters during the program by ELFFAN I FiSAT for Carp and Roach obtained from von Bertalanffy growth function were L_∞ =70.78 and 32.39 cm, K = 1.24/year and 1.24/year; respectively. the growth parameters such as infinite length( L_∞), growth coefficient (K) and t0 age zero base on One Brtalnfy equation estimated, 78.70, 0.14, 32.39, 0.5 ,-0.5 Total mortality coefficient Using the method of catch curve (Z), natural mortality rate using the empirical formula Pauli( M), fishing mortality (F) using the formula = Z - M and Growth performance index (Ǿ) for Carp and Roach were obtained 1.5year^-1, 0.9year^-1, 0.5year^-1, 0.26year^-1, 1.24, 0.4, 2.85 and 2.54 respectively. The biomass (B) and Maximum Sustainable Yield (MSY) with the present effort for Carp and Roach were estimated 1628.7t, 368.9t and88.06t, and 32.7t respectively.

This study can be used as a basis for its management in the future. The purpose of the study was "Analyze the dynamics of white shrimp populations such as growth patterns, long frequency distribution, growth parameters, mortality rates, and recruitment patterns. The study was conducted for 12 months, from January-December 2021. The method used in sampling is purposive sampling, using a mini sine purse fishing gear measuring 8 m x 2 m. White shrimp growth patterns were analyzed using FISAT 2 software, shell length frequency distribution and growth parameters using von Bertalanffy growth models, asymptotic length and growth coefficient using the ELEFAN method, total mortality rate, natural and capture using Pauly's empirical equations, and recruitment patterns using the FAO-ICLARM program. The results of the study found the highest density of white shrimp at station 3 (257 ind / m2) and the lowest at station 2 (213 ind / m2). The distribution pattern of white shrimp at each station is in groups. The growth pattern of white shrimp is allometric negative. The theoretical lifespan at the length of zero carapace is 0.02 years or 0.2 months. The natural mortality rate of white shrimp is 0.75/year, the total mortality rate is 0.91/year and the catch mortality rate is 0.16/year. The capture of white shrimp in the waters of Karang Gading Deli Serdang Regency, North Sumatra is still under exploitation. Peak recruitment was in May and June (18.92% and 18.33%) after four months of white shrimp spawning.

The statistic shows the 20 countries with the lowest fertility rates in 2024. All figures are estimates. In 2024, the fertility rate in Taiwan was estimated to be at 1.11 children per woman, making it the lowest fertility rate worldwide. Fertility rate The fertility rate is the average number of children born per woman of child-bearing age in a country. Usually, a woman aged between 15 and 45 is considered to be in her child-bearing years. The fertility rate of a country provides an insight into its economic state, as well as the level of health and education of its population. Developing countries usually have a higher fertility rate due to lack of access to birth control and contraception, and to women usually foregoing a higher education, or even any education at all, in favor of taking care of housework. Many families in poorer countries also need their children to help provide for the family by starting to work early and/or as caretakers for their parents in old age. In developed countries, fertility rates and birth rates are usually much lower, as birth control is easier to obtain and women often choose a career before becoming a mother. Additionally, if the number of women of child-bearing age declines, so does the fertility rate of a country. As can be seen above, countries like Hong Kong are a good example for women leaving the patriarchal structures and focusing on their own career instead of becoming a mother at a young age, causing a decline of the country’s fertility rate. A look at the fertility rate per woman worldwide by income group also shows that women with a low income tend to have more children than those with a high income. The United States are neither among the countries with the lowest, nor among those with the highest fertility rate, by the way. At 2.08 children per woman, the fertility rate in the US has been continuously slightly below the global average of about 2.4 children per woman over the last decade.

This dataset explores the United States Department of Agriculture (USDA) Food and Nutrition Service Program - Food Stamp Program by recording the number of persons participating in February 2007, January 2008 and February 2008. Then, a calculation of change over time is achieved. * The following areas receive Nutrition Assistance Grants which provide benefits analogous to the Food Stamp Program: Puerto Rico, American Samoa, and the Northern Marianas. January and February 2008 data are preliminary and are subject to significant revision.

Native Hawaiian and Pacific Islander women had the highest fertility rate of any ethnicity in the United States in 2022, with about 2,237.5 births per 1,000 women. The fertility rate for all ethnicities in the U.S. was 1,656.5 births per 1,000 women. What is the total fertility rate? The total fertility rate is an estimation of the number of children who would theoretically be born per 1,000 women through their childbearing years (generally considered to be between the ages of 15 and 44) according to age-specific fertility rates. The fertility rate is different from the birth rate, in that the birth rate is the number of births in relation to the population over a specific period of time. Fertility rates around the world Fertility rates around the world differ on a country-by-country basis, and more industrialized countries tend to see lower fertility rates. For example, Niger topped the list of the countries with the highest fertility rates, and Taiwan had the lowest fertility rate.

Index of monthly average price for regular fuel in major US Cities from 1995 to April 2008. Months are numbered. For example: 1995M09 = September 1995 Data was extracted from the Consumer Price Index. Polygons were extracted from the Office of Management and Budget's breakdown of Metropolitan Statistical Areas from 1996