This table contains 2394 series, with data for years 1991 - 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Population group (19 items: Entire cohort; Income adequacy quintile 1 (lowest);Income adequacy quintile 2;Income adequacy quintile 3 ...), Age (14 items: At 25 years; At 30 years; At 40 years; At 35 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Life expectancy; High 95% confidence interval; life expectancy; Low 95% confidence interval; life expectancy ...).

Life expectancy at birth and at age 65, by sex, on a three-year average basis.

Life expectancy is the number of years a person would be expected to live, starting from birth (for life expectancy at birth) or at age 65 (for life expectancy at age 65), on the basis of the mortality statistics for a given observation period. Life expectancy is a widely used indicator of the health of a population. Life expectancy measures quantity rather than quality of life.

In 2023, the average life expectancy of the world was 70 years for men and 75 years for women. The lowest life expectancies were found in Africa, while Oceania and Europe had the highest.

What is life expectancy?

Life expectancy is defined as a statistical measure of how long a person may live, based on demographic factors such as gender, current age, and most importantly the year of their birth. The most commonly used measure of life expectancy is life expectancy at birth or at age zero. The calculation is based on the assumption that mortality rates at each age were to remain constant in the future.

Life expectancy has changed drastically over time, especially during the past 200 years. In the early 20th century, the average life expectancy at birth in the developed world stood at 31 years. It has grown to an average of 70 and 75 years for males and females respectively, and is expected to keep on growing with advances in medical treatment and living standard continuing.

Highest and lowest life expectancy worldwide

Life expectancy still varies greatly between different regions and countries of the world. The biggest impact on life expectancy is the quality of public health, medical care, and diet. As of 2021, the countries with the highest life expectancy were Japan, Liechtenstein, Switzerland, and South Korea, all at 84 years. Most of the countries with the lowest life expectancy are mostly African countries. The ranking was led by the Chad, Nigeria, and Lesotho with 53 years.

This table contains mortality indicators by sex for Canada and all provinces except Prince Edward Island. These indicators are derived from three-year complete life tables. Mortality indicators derived from single-year life tables are also available (table 13-10-0837). For Prince Edward Island, Yukon, the Northwest Territories and Nunavut, mortality indicators derived from three-year abridged life tables are available (table 13-10-0140).

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

World Population Data from the United Nations (UN), United Nations Department of Economic and Social Affairs Population Division World Population Prospects 2022

Notes
File (CSV, 6 KB) Location notes.

**Demographic Indicators ** Indicator reference (CSV, 4 KB) 1950-2100, medium (ZIP, 7.77 MB) 2022-2100, other scenarios (ZIP, 34.76 MB) Demographic Indicators:

Total Population, as of 1 January (thousands)
Total Population, as of 1 July (thousands)
Male Population, as of 1 July (thousands)
Female Population, as of 1 July (thousands)
Population Density, as of 1 July (persons per square km)
Population Sex Ratio, as of 1 July (males per 100 females)
Median Age, as of 1 July (years)
Natural Change, Births minus Deaths (thousands)
Rate of Natural Change (per 1,000 population)
Population Change (thousands)
Population Growth Rate (percentage)
Population Annual Doubling Time (years)
Births (thousands)
Births by women aged 15 to 19 (thousands)
Crude Birth Rate (births per 1,000 population)
Total Fertility Rate (live births per woman)
Net Reproduction Rate (surviving daughters per woman)
Mean Age Childbearing (years)
Sex Ratio at Birth (males per 100 female births)
Total Deaths (thousands)
Male Deaths (thousands)
Female Deaths (thousands)
Crude Death Rate (deaths per 1,000 population)
Life Expectancy at Birth, both sexes (years)
Male Life Expectancy at Birth (years)
Female Life Expectancy at Birth (years)
Life Expectancy at Age 15, both sexes (years)
Male Life Expectancy at Age 15 (years)
Female Life Expectancy at Age 15 (years)
Life Expectancy at Age 65, both sexes (years)
Male Life Expectancy at Age 65 (years)
Female Life Expectancy at Age 65 (years)
Life Expectancy at Age 80, both sexes (years)
Male Life Expectancy at Age 80 (years)
Female Life Expectancy at Age 80 (years)
Infant Deaths, under age 1 (thousands)
Infant Mortality Rate (infant deaths per 1,000 live births)
Live births Surviving to Age 1 (thousands)
Deaths under age 5 (thousands)
Under-five Mortality Rate (deaths under age 5 per 1,000 live births)
Mortality before Age 40, both sexes (deaths under age 40 per 1,000 live births)
Male mortality before Age 40 (deaths under age 40 per 1,000 male live births)
Female mortality before Age 40 (deaths under age 40 per 1,000 female live births)
Mortality before Age 60, both sexes (deaths under age 60 per 1,000 live births)
Male mortality before Age 60 (deaths under age 60 per 1,000 male live births)
Female mortality before Age 60 (deaths under age 60 per 1,000 female live births)
Mortality between Age 15 and 50, both sexes (deaths under age 50 per 1,000 alive at age 15)
Male mortality between Age 15 and 50 (deaths under age 50 per 1,000 males alive at age 15)
Female mortality between Age 15 and 50 (deaths under age 50 per 1,000 females alive at age 15)
Mortality between Age 15 and 60, both sexes (deaths under age 60 per 1,000 alive at age 15)
Male mortality between Age 15 and 60 (deaths under age 60 per 1,000 males alive at age 15)
Female mortality between Age 15 and 60 (deaths under age 60 per 1,000 females alive at age 15)
Net Number of Migrants (thousands)
Net Migration Rate (per 1,000 population)

Fertility
1950-2100, single age (ZIP, 78.01 MB) 1950-2100, 5-year age groups (ZIP, 22.38 MB)

Age-specific Fertility Rate (ASFR)
Percent Age-specific Fertility Rate (PASFR)
Births (thousands)

**Life Tables ** 1950-2021, medium (ZIP, 68.72 MB) 2022-2100, medium (ZIP, 74.62 MB) Abridged life tables up to age 100 by sex and both sexes combined providing a set of values showing the mortality experience of a hypothetical group of infants born at the same time and subject throughout their lifetime to the specific mortality rates of a given year, from 1950 to 2100. Only medium is available.

mx: Central death rate, nmx, for the age interval (x, x+n)
qx: Probability of dying (nqx), for an individual between age x and x+n
px: Probability of surviving, (npx), for an individual of age x to age x+n
lx: Number of survivors, (lx), at age (x) for 100000 births
dx: Number of deaths, (ndx), between ages x and x+n
Lx: Number of person-years lived, (nLx), between ages x and x+n
Sx: Survival ratio (nSx) corresponding to proportion of the life table population in age group (x, x+n) who are alive n year later
Tx: Person-years lived, (Tx), above age x
ex: Expectation of life (ex) at age x, i.e., average number of years lived subsequent to age x by those reaching age x
ax: Average number of years lived (nax) between ages x and x+n by those dying in the interval

Life Tables 1950-2021 (ZIP, 94.76 MB) 2022-2100 (ZIP, 101.66 MB) Single age life tables up to age 10...

Bivariate and multivariable regression analysis of mortality and incidence per million with three independent variables: Days from the 22nd of January with break point at day 50 (time interval 31–50 and 51–73 days), life expectancy, and outpatients contacts per person per year.

This dataset supports the findings outlined in Regev, J., Zaar, J., Relaño-Iborra, H., and Dau, T. (2025). "Investigating the effects of age and hearing loss on speech intelligibility and amplitude modulation frequency selectivity." The Journal of the Acoustical Society of America, 157(3), 2077-2090. https://doi.org/10.1121/10.0036220The dataset contains data for 10 young and 9 older listeners with normal hearing (young and older NH listeners), as well as 9 older listeners with hearing impairment (older HI listeners).The Readme file provides a description of the dataset's content, and of the related article.The data collected were:Population data: age, tested ear, and reverse digit span scoreAudiogramsSpeech-reception thresholds (SRTs)Average dynamic ranges of masked-threshold patterns (MTPs)TP numbers across datasets (providing the corresponding TP numbers for the present dataset and those of Regev et al., 2024a and Regev et al., 2024b).Population DataDataset giving the population information for the 28 participants (tp), split into young NH, older NH, and older HI (group), tested in the study. The data summarizes the participants' age, tested ear (ear), and their Reverse Digit Span score (rds_score). The age is given in years and the reverse digit span score is given on a normalized scale from 0 to 1.The reverse digit span scores are re-used from Regev et al. (2024a; 2024b).AudiogramAudiometric thresholds (thresh) were collected for 28 participants (tp), split into young NH, older NH, and older HI (group), at frequencies (freq) of 0.125, 0.25, 0.5, 1, 2, 3, 4, 6, and 8 kHz. The thresholds are given in dB Hearing Level (HL).Speech-Reception Thresholds (SRTs)Speech-reception thresholds (SRT) at the 50%-correct point were collected 28 participants (tp), split into young NH, older NH, and older HI (group). The SRTs are given in dB signal-to-noise ratio (SNR). The test used the Danish Hearing in Noise Test (HINT; Nielsen & Dau, 2011).Five different maskers (conditions) were used:a speech-shaped noise (SSN)the ICRA-5 noise (ICRA; Dreschler et al., 2001)a male competing talker (Male comp)a female competing talker (Female comp)a cocktail-party scenario (Cocktail).A detailed description of each masker is available in the article. For each condition, the SRT was assessed twice (repetition), each time using a different list (list) from the target speech corpus. The SRTs were then averaged across repetitions.Dynamic Range of Masked-Threshold Patterns (MTPs)Regev et al. (2024a; 2024b) collected masked-threshold patterns (MTPs) for the 28 participants (tp), split into young NH, older NH, and older HI (group).MTPs were collected at four different target modulation frequencies (fmod) of 4, 16, 64, and 128 Hz.Here, the average dynamic range (dyn_range) of the MTP at the 4-Hz target modulation frequency (fmod) was derived for each participant. For each participant, the peak of the MTP was identified as the maximum threshold. The difference between the peak and the minimum threshold on each side of the peak was then computed, and the average dynamic range was finally calculated as the mean between the differences on both sides. If a single side of the peak was identified, then he threshold difference on that side was taken as the dynamic range.Masked thresholds for TP23 could not be could not be obtained for the 4-Hz target modulation frequency by Regev et al. (2024b; where the participant was labeled TP07). Hence, the dynamic range was registered as NaN.TP numbers across datasetsThe participant in this study previously provided data reported in the datasets by Regev et al. (2024a, 2024b). Some of these data were re-used in this study, either directly (i.e., the RDS scores) or to derive new measures (i.e., the MTPs to derive the dynamic ranges). This sheet provides the correspondence of the TP numbers between this dataset (tp) and those of Regev et al. (2024a, 2024b; tp_Regev_2024a and tp_Regev_2024b, respectively), for each listener group (group). The sheet states NA in case the participant was not included in the previous dataset.Ethical statementAll listeners were financially compensated for their time and gave written informed consent. Ethical approval for the study was provided by the Science-Ethics Committee for the Capital Region of Denmark (reference H-16036391).AcknowledgmentsThe authors thank Borgný Súsonnudóttir Hansen for her contribution to the data collection. The authors also thank Christian Stender Simonsen for kindly sharing the experimental framework used to run the listening test, as well as several of the signal recordings, and Jens Hjortkjær and Jonatan Märcher-Rørsted for kindly providing their implementations of the reverse digit span test and of the Cambridge formula (CamEq).ReferencesDreschler, W. A., Verschuure, H., Ludvigsen, C., & Westermann, S. (2001). ICRA Noises: Artificial Noise Signals with Speech-like Spectral and Temporal Properties for Hearing Instrument Assessment. Audiol, 40(3), 148–157. https://doi.org/10.3109/00206090109073110Nielsen J. B. & Dau T. (2011). The Danish hearing in noise test. Int J Audiol. 50(3):202-8. https://doi.org/10.3109/14992027.2010.524254Regev, J., Zaar, J.; Relaño-Iborra, H., & Dau, T. (2024a). Dataset for: "Age-related reduction of amplitude modulation frequency selectivity". Technical University of Denmark. Dataset. https://doi.org/10.11583/DTU.25134527Regev, J., Relaño-Iborra, H., Zaar, J., & Dau, T.(2024b). Dataset for: "Disentangling the effects of hearing loss and age on amplitude modulation frequency selectivity". Technical University of Denmark. Dataset. https://doi.org/10.11583/DTU.25134611

Sex differences in mortality are pervasive in vertebrates, and usually result in shorter life spans in the larger sex, although the underlying mechanisms are still unclear. On the other hand, differences in frailty among individuals (i.e. individual heterogeneity), can play a major role in shaping demographic trajectories in wild populations. The link between these two processes has seldom been explored. We used Bayesian survival trajectory analysis to study age-specific mortality trajectories in the Eurasian sparrowhawk (Accipiter nisus), a monogamous raptor with reversed sexual size dimorphism. We tested the effect of individual heterogeneity on age-specific mortality, and the extent by which this heterogeneity was determined by average reproductive output and wing length as measures of an individual's frailty. We found that sex differences in age-specific mortality were primarily driven by the differences in individual heterogeneity between the two sexes. Females were more heterogeneous than males in their level of frailty. Thus, a larger number of females with low frailty are able to survive to older ages than males, with life expectancy for the least frail adult females reaching up to 4·23 years, while for the least frail adult males it was of 2·68 years. We found that 50% of this heterogeneity was determined by average reproductive output and wing length in both sexes. For both, individuals with high average reproductive output had also higher chances to survive. However, the effect of wing length was different between the two sexes. While larger females had higher survival, larger males had lower chances to survive. Our results contribute a novel perspective to the ongoing debate about the mechanisms that drive sex differences in vital rates in vertebrates. Although we found that variables that relate to the cost of reproduction and sexual dimorphism are at least partially involved in determining these sex differences, it is through their effect on the level of frailty that they affect age patterns of mortality. Therefore, our results raise the possibility that observed differences in age-specific demographic rates may in fact be driven by differences in individual heterogeneity. SparrowhawkDatDryadThis is a 28 years (1971-1999) capture-mark-recapture dataset gathered by the British ornithologist Ian Newton from two populations of the Eurasian sparrowhawk in the United Kingdom (Lakhani & Newton, 1983; Newton & Rothery, 1997; Newton, Rothery, & Wyllie, 2008). Until recently, these data were freely available as part of the Long-term Individual based Time Series (LITS) project (Jones, Clutton-Brock, Coulson, & Godfray, 2008). For a description of the dataset please see ReadMe file.

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

Malaysia life expectancy for 2025 is 76.93, a 0.18% increase from 2024.

<ul style='margin-top:20px;'>

<li>Malaysia life expectancy for 2024 was <strong>76.79</strong>, a <strong>0.18% increase</strong> from 2023.</li>
<li>Malaysia life expectancy for 2023 was <strong>76.66</strong>, a <strong>1.61% increase</strong> from 2022.</li>
<li>Malaysia life expectancy for 2022 was <strong>75.44</strong>, a <strong>2.07% increase</strong> from 2021.</li>
</ul>Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.

Pearson’s correlation coefficients were used for quantitative variables and normally distributed variables. Spearman’s correlation coefficients were used for categorical variables and not normally distributed variables.

In 2022, the infant mortality rate in India was at about 25.5 deaths per 1,000 live births, a significant decrease from previous years. Infant mortality as an indicatorThe infant mortality rate is the number of deaths of children under one year of age per 1,000 live births. This rate is an important key indicator for a country’s health and standard of living; a low infant mortality rate indicates a high standard of healthcare. Causes of infant mortality include premature birth, sepsis or meningitis, sudden infant death syndrome, and pneumonia. Globally, the infant mortality rate has shrunk from 63 infant deaths per 1,000 live births to 27 since 1990 and is forecast to drop to 8 infant deaths per 1,000 live births by the year 2100. India’s rural problemWith 32 infant deaths per 1,000 live births, India is neither among the countries with the highest nor among those with the lowest infant mortality rate. Its decrease indicates an increase in medical care and hygiene, as well as a decrease in female infanticide. Increasing life expectancy at birth is another indicator that shows that the living conditions of the Indian population are improving. Still, India’s inhabitants predominantly live in rural areas, where standards of living as well as access to medical care and hygiene are traditionally lower and more complicated than in cities. Public health programs are thus put in place by the government to ensure further improvement.