The countries with the lowest life expectancy worldwide include the Nigeria, Chad, and Lesotho. As of 2023, people born in Nigeria could be expected to live only up to ** years. This is almost ** years shorter than the global life expectancy. Life expectancy The global life expectancy has gradually increased over the past couple decades, rising from **** years in 2011 to **** years in 2023. However, the years 2020 and 2021 saw a decrease in global life expectancy due to the COVID-19 pandemic. Furthermore, life expectancy can vary greatly depending on the country and region. For example, all the top 20 countries with the lowest life expectancy worldwide are in Africa. The countries with the highest life expectancy include Liechtenstein, Switzerland, and Japan. Causes of death The countries with the lowest life expectancy worldwide are all low-income or developing countries that lack health care access and treatment that more developed countries can provide. The leading causes of death in these countries therefore differ from those of middle-income and upper-income countries. The leading causes of death in low-income countries include diseases such as HIV/AIDS and malaria, as well as preterm birth complications, which do not cause substantial death in higher income countries.

In 2024, the average life expectancy for those born in more developed countries was 76 years for men and 82 years for women. On the other hand, the respective numbers for men and women born in the least developed countries were 64 and 69 years. Improved health care has lead to higher life expectancy Life expectancy is the measure of how long a person is expected to live. Life expectancy varies worldwide and involves many factors such as diet, gender, and environment. As medical care has improved over the years, life expectancy has increased worldwide. Introduction to health care such as vaccines has significantly improved the lives of millions of people worldwide. The average worldwide life expectancy at birth has steadily increased since 2007, but dropped during the COVID-19 pandemic in 2020 and 2021. Life expectancy worldwide More developed countries tend to have higher life expectancies, for a multitude of reasons. Health care infrastructure and quality of life tend to be higher in more developed countries, as is access to clean water and food. Africa was the continent that had the lowest life expectancy for both men and women in 2023, while Oceania had the highest for men and Europe and Oceania had the highest for women.

Graph and download economic data for Life Expectancy at Birth, Total for Least Developed Countries (SPDYNLE00INLDC) from 1960 to 2023 about life expectancy, life, and birth.

Men born in Chad have the lowest life expectancy in the world as of 2024, reaching only ** years. The lowest life expectancy for women in the world in 2024 was for girls born in Nigeria, with only ** years. Except for Afghanistan, all the countries with the lowest life expectancy in the world are in Africa.

United States - Life Expectancy at Birth, Total for Least Developed Countries was 66.51610 Number of Years in January of 2023, according to the United States Federal Reserve. Historically, United States - Life Expectancy at Birth, Total for Least Developed Countries reached a record high of 66.51610 in January of 2023 and a record low of 35.41296 in January of 1950. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Life Expectancy at Birth, Total for Least Developed Countries - last updated from the United States Federal Reserve on August of 2025.

As of 2023, the countries with the highest life expectancy included Switzerland, Japan, and Spain. As of that time, a new-born child in Switzerland could expect to live an average of **** years. Around the world, females consistently have a higher average life expectancy than males, with females in Europe expected to live an average of *** years longer than males on this continent. Increases in life expectancy The overall average life expectancy in OECD countries increased by **** years from 1970 to 2019. The countries that saw the largest increases included Turkey, India, and South Korea. The life expectancy at birth in Turkey increased an astonishing 24.4 years over this period. The countries with the lowest life expectancy worldwide as of 2022 were Chad, Lesotho, and Nigeria, where a newborn could be expected to live an average of ** years. Life expectancy in the U.S. The life expectancy in the United States was ***** years as of 2023. Shockingly, the life expectancy in the United States has decreased in recent years, while it continues to increase in other similarly developed countries. The COVID-19 pandemic and increasing rates of suicide and drug overdose deaths from the opioid epidemic have been cited as reasons for this decrease.

As of 2023, the countries with the highest life expectancy included Liechtenstein, Switzerland, and Japan. In Japan, a person could expect to live up to around ** years. In general, the life expectancy for females is higher than that of males, with lifestyle choices and genetics the two major determining factors of life expectancy. Life expectancy worldwide The overall life expectancy worldwide has increased since the development of modern medicine and technology. In 2011, the global life expectancy was **** years. By 2023, it had increased to **** years. However, the years 2020 and 2021 saw a decline in global life expectancy due to the COVID-19 pandemic. Furthermore, not every country has seen a substantial increase in life expectancy. In Nigeria, for example, the life expectancy is only ** years, almost ***years shorter than the global average. In addition to Nigeria, the countries with the shortest life expectancy include Chad, Lesotho, and the Central African Republic. Life expectancy in the U.S. In the United States, life expectancy at birth is currently ***** years. Life expectancy in the U.S. generally increases every year, however, over the past decade, life expectancy has seen some surprising decreases. The major contributing factors to this drop have been the ongoing opioid epidemic, which claimed around ****** lives in 2022 alone, and the COVID-19 pandemic.

This statistic shows the countries and territories with the lowest projected life expectancy between 2050 and 2055. Between 2050 and 2055, the Central African Republic is projected to have a life expectancy of 62.13 years.

In the World Health Organization (WHO)-coordinated Cardiovascular Disease and Alimentary Comparison Study, isoflavones (I; biomarker for dietary soy) and taurine (T; biomarker for dietary fish) in 24-hour—urine (24U) were inversely related to coronary heart disease (CHD) mortality. High levels of these biomarkers are found in Japanese people, whose CHD mortality is lowest among developed countries. We analyzed the association of these biomarkers with cardiovascular disease risk in the Japanese to know their health effects within one ethnic population. First, to compare the Japanese intake of I and T with international intakes, the ratios of 24UI and 24UT to creatinine from the WHO Study were divided into quintiles for analysis. The ratio for the Japanese was the highest in the highest quintiles for both I and T, reaching 88.1%, far higher than the average ratio for the Japanese (26.3%) in the total study population. Second, 553 inhabitants of Hyogo Prefecture, Japan, aged 30 to 79 years underwent 24-U collection and blood analyses. The 24UT and 24UI were divided into tertiles and adjusted for age and sex. The highest T tertile, compared with the lowest tertile, showed significantly higher levels of high-density lipoprotein-cholesterol (HDL-C), total cholesterol, 24U sodium (Na) and potassium (K). The highest I tertile showed significantly higher folate, 24UNa and 24UK compared with the lowest tertile. The highest tertile of both T and I showed significantly higher HDL-C, folate, and 24UNa and 24UK compared with the lowest tertile. Thus, greater consumption of fish and soy were significantly associated with higher HDL-C and folate levels, possibly a contributor to Japan having the lowest CHD mortality and longest life expectancy among developed countries. As these intakes were also associated with a high intake of salt, a low-salt intake of fish and soy should be recommended for healthy life expectancy.

In 2024, the average life expectancy in the world was 71 years for men and 76 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 standards 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 2022, the countries with the highest life expectancy were Japan, Liechtenstein, Switzerland, and Australia, all at 84–83 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 53–54 years.

Goal 10Reduce inequality within and among countriesTarget 10.1: By 2030, progressively achieve and sustain income growth of the bottom 40 per cent of the population at a rate higher than the national averageIndicator 10.1.1: Growth rates of household expenditure or income per capita among the bottom 40 per cent of the population and the total populationSI_HEI_TOTL: Growth rates of household expenditure or income per capita (%)Target 10.2: By 2030, empower and promote the social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other statusIndicator 10.2.1: Proportion of people living below 50 per cent of median income, by sex, age and persons with disabilitiesSI_POV_50MI: Proportion of people living below 50 percent of median income (%)Target 10.3: Ensure equal opportunity and reduce inequalities of outcome, including by eliminating discriminatory laws, policies and practices and promoting appropriate legislation, policies and action in this regardIndicator 10.3.1: Proportion of population reporting having personally felt discriminated against or harassed in the previous 12 months on the basis of a ground of discrimination prohibited under international human rights lawVC_VOV_GDSD: Proportion of population reporting having felt discriminated against, by grounds of discrimination, sex and disability (%)Target 10.4: Adopt policies, especially fiscal, wage and social protection policies, and progressively achieve greater equalityIndicator 10.4.1: Labour share of GDPSL_EMP_GTOTL: Labour share of GDP (%)Indicator 10.4.2: Redistributive impact of fiscal policySI_DST_FISP: Redistributive impact of fiscal policy, Gini index (%)Target 10.5: Improve the regulation and monitoring of global financial markets and institutions and strengthen the implementation of such regulationsIndicator 10.5.1: Financial Soundness IndicatorsFI_FSI_FSANL: Non-performing loans to total gross loans (%)FI_FSI_FSERA: Return on assets (%)FI_FSI_FSKA: Regulatory capital to assets (%)FI_FSI_FSKNL: Non-performing loans net of provisions to capital (%)FI_FSI_FSKRTC: Regulatory Tier 1 capital to risk-weighted assets (%)FI_FSI_FSLS: Liquid assets to short term liabilities (%)FI_FSI_FSSNO: Net open position in foreign exchange to capital (%)Target 10.6: Ensure enhanced representation and voice for developing countries in decision-making in global international economic and financial institutions in order to deliver more effective, credible, accountable and legitimate institutionsIndicator 10.6.1: Proportion of members and voting rights of developing countries in international organizationsSG_INT_MBRDEV: Proportion of members of developing countries in international organizations, by organization (%)SG_INT_VRTDEV: Proportion of voting rights of developing countries in international organizations, by organization (%)Target 10.7: Facilitate orderly, safe, regular and responsible migration and mobility of people, including through the implementation of planned and well-managed migration policiesIndicator 10.7.1: Recruitment cost borne by employee as a proportion of monthly income earned in country of destinationIndicator 10.7.2: Number of countries with migration policies that facilitate orderly, safe, regular and responsible migration and mobility of peopleSG_CPA_MIGRP: Proportion of countries with migration policies to facilitate orderly, safe, regular and responsible migration and mobility of people, by policy domain (%)SG_CPA_MIGRS: Countries with migration policies to facilitate orderly, safe, regular and responsible migration and mobility of people, by policy domain (1 = Requires further progress; 2 = Partially meets; 3 = Meets; 4 = Fully meets)Indicator 10.7.3: Number of people who died or disappeared in the process of migration towards an international destinationiSM_DTH_MIGR: Total deaths and disappearances recorded during migration (number)Indicator 10.7.4: Proportion of the population who are refugees, by country of originSM_POP_REFG_OR: Number of refugees per 100,000 population, by country of origin (per 100,000 population)Target 10.a: Implement the principle of special and differential treatment for developing countries, in particular least developed countries, in accordance with World Trade Organization agreementsIndicator 10.a.1: Proportion of tariff lines applied to imports from least developed countries and developing countries with zero-tariffTM_TRF_ZERO: Proportion of tariff lines applied to imports with zero-tariff (%)Target 10.b: Encourage official development assistance and financial flows, including foreign direct investment, to States where the need is greatest, in particular least developed countries, African countries, small island developing States and landlocked developing countries, in accordance with their national plans and programmesIndicator 10.b.1: Total resource flows for development, by recipient and donor countries and type of flow (e.g. official development assistance, foreign direct investment and other flows)DC_TRF_TOTDL: Total assistance for development, by donor countries (millions of current United States dollars)DC_TRF_TOTL: Total assistance for development, by recipient countries (millions of current United States dollars)DC_TRF_TFDV: Total resource flows for development, by recipient and donor countries (millions of current United States dollars)Target 10.c: By 2030, reduce to less than 3 per cent the transaction costs of migrant remittances and eliminate remittance corridors with costs higher than 5 per centIndicator 10.c.1: Remittance costs as a proportion of the amount remittedSI_RMT_COST: Remittance costs as a proportion of the amount remitted (%)SI_RMT_COST_BC: Corridor remittance costs as a proportion of the amount remitted (%)SI_RMT_COST_SC: SmaRT corridor remittance costs as a proportion of the amount remitted (%)

UN Environment Programme - a Custodian Agency - Mandate General Assembly Resolution 71/313 - Work of the Statistical Commission pertaining to the 2030 Agenda for Sustainable Development INDICATOR METHODOLOGIES,COMPARABLE DATA 7. Urges international organizations to base the global review on data produced by national statistical systems and, if specific country data are not available for reliable estimation, to consult with concerned countries to produce and validate modelled estimates before publication, urges that communication and coordination among international organizations be enhanced in order to avoid duplicate reports, ensure consistency of data and reduce response burdens on countries, and urges international organizations to provide the methodologies used to harmonize country data for international comparability and produce estimates through transparent mechanisms; CAPACITY DEVELOPMENT 11. Urges countries, the United Nations funds and programmes, the specialized agencies, the Secretariat, including the regional commissions, the Bretton Woods institutions, international organizations and bilateral and regional funding agencies to intensify their support for strengthening data collection and statistical capacity-building, including capacity-building that strengthens coordination among national statistical offices, as appropriate and within their mandates, in a coordinated manner that recognizes national priorities and reflects national ownership of the implementation of the 2030 Agenda for Sustainable Development, in developing countries, particularly African countries, least developed countries, landlocked developing countries, small island developing States, middle-income countries, countries in situations of conflict and post-conflict countries, using all available means of support.

Goal 1End poverty in all its forms everywhereTarget 1.1: By 2030, eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.25 a dayIndicator 1.1.1: Proportion of the population living below the international poverty line by sex, age, employment status and geographic location (urban/rural)SI_POV_DAY1: Proportion of population below international poverty line (%)SI_POV_EMP1: Employed population below international poverty line, by sex and age (%)Target 1.2: By 2030, reduce at least by half the proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitionsIndicator 1.2.1: Proportion of population living below the national poverty line, by sex and ageSI_POV_NAHC: Proportion of population living below the national poverty line (%)Indicator 1.2.2: Proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitionsSD_MDP_MUHC: Proportion of population living in multidimensional poverty (%)SD_MDP_ANDI: Average proportion of deprivations for people multidimensionally poor (%)SD_MDP_MUHHC: Proportion of households living in multidimensional poverty (%)SD_MDP_CSMP: Proportion of children living in child-specific multidimensional poverty (%)Target 1.3: Implement nationally appropriate social protection systems and measures for all, including floors, and by 2030 achieve substantial coverage of the poor and the vulnerableIndicator 1.3.1: Proportion of population covered by social protection floors/systems, by sex, distinguishing children, unemployed persons, older persons, persons with disabilities, pregnant women, newborns, work-injury victims and the poor and the vulnerableSI_COV_MATNL: [ILO] Proportion of mothers with newborns receiving maternity cash benefit (%)SI_COV_POOR: [ILO] Proportion of poor population receiving social assistance cash benefit, by sex (%)SI_COV_SOCAST: [World Bank] Proportion of population covered by social assistance programs (%)SI_COV_SOCINS: [World Bank] Proportion of population covered by social insurance programs (%)SI_COV_CHLD: [ILO] Proportion of children/households receiving child/family cash benefit, by sex (%)SI_COV_UEMP: [ILO] Proportion of unemployed persons receiving unemployment cash benefit, by sex (%)SI_COV_VULN: [ILO] Proportion of vulnerable population receiving social assistance cash benefit, by sex (%)SI_COV_WKINJRY: [ILO] Proportion of employed population covered in the event of work injury, by sex (%)SI_COV_BENFTS: [ILO] Proportion of population covered by at least one social protection benefit, by sex (%)SI_COV_DISAB: [ILO] Proportion of population with severe disabilities receiving disability cash benefit, by sex (%)SI_COV_LMKT: [World Bank] Proportion of population covered by labour market programs (%)SI_COV_PENSN: [ILO] Proportion of population above statutory pensionable age receiving a pension, by sex (%)Target 1.4: By 2030, ensure that all men and women, in particular the poor and the vulnerable, have equal rights to economic resources, as well as access to basic services, ownership and control over land and other forms of property, inheritance, natural resources, appropriate new technology and financial services, including microfinanceIndicator 1.4.1: Proportion of population living in households with access to basic servicesSP_ACS_BSRVH2O: Proportion of population using basic drinking water services, by location (%)SP_ACS_BSRVSAN: Proportion of population using basic sanitation services, by location (%)Indicator 1.4.2: Proportion of total adult population with secure tenure rights to land, (a) with legally recognized documentation, and (b) who perceive their rights to land as secure, by sex and type of tenureSP_LGL_LNDDOC: Proportion of people with legally recognized documentation of their rights to land out of total adult population, by sex (%)SP_LGL_LNDSEC: Proportion of people who perceive their rights to land as secure out of total adult population, by sex (%)SP_LGL_LNDSTR: Proportion of people with secure tenure rights to land out of total adult population, by sex (%)Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disastersIndicator 1.5.1: Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 populationVC_DSR_MISS: Number of missing persons due to disaster (number)VC_DSR_AFFCT: Number of people affected by disaster (number)VC_DSR_MORT: Number of deaths due to disaster (number)VC_DSR_MTMP: Number of deaths and missing persons attributed to disasters per 100,000 population (number)VC_DSR_MMHN: Number of deaths and missing persons attributed to disasters (number)VC_DSR_DAFF: Number of directly affected persons attributed to disasters per 100,000 population (number)VC_DSR_IJILN: Number of injured or ill people attributed to disasters (number)VC_DSR_PDAN: Number of people whose damaged dwellings were attributed to disasters (number)VC_DSR_PDYN: Number of people whose destroyed dwellings were attributed to disasters (number)VC_DSR_PDLN: Number of people whose livelihoods were disrupted or destroyed, attributed to disasters (number)Indicator 1.5.2: Direct economic loss attributed to disasters in relation to global gross domestic product (GDP)VC_DSR_GDPLS: Direct economic loss attributed to disasters (current United States dollars)VC_DSR_LSGP: Direct economic loss attributed to disasters relative to GDP (%)VC_DSR_AGLH: Direct agriculture loss attributed to disasters (current United States dollars)VC_DSR_HOLH: Direct economic loss in the housing sector attributed to disasters (current United States dollars)VC_DSR_CILN: Direct economic loss resulting from damaged or destroyed critical infrastructure attributed to disasters (current United States dollars)VC_DSR_CHLN: Direct economic loss to cultural heritage damaged or destroyed attributed to disasters (millions of current United States dollars)VC_DSR_DDPA: Direct economic loss to other damaged or destroyed productive assets attributed to disasters (current United States dollars)Indicator 1.5.3: Number of countries that adopt and implement national disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015–2030SG_DSR_LGRGSR: Score of adoption and implementation of national DRR strategies in line with the Sendai FrameworkSG_DSR_SFDRR: Number of countries that reported having a National DRR Strategy which is aligned to the Sendai FrameworkIndicator 1.5.4: Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategiesSG_DSR_SILS: Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies (%)SG_DSR_SILN: Number of local governments that adopt and implement local DRR strategies in line with national strategies (number)SG_GOV_LOGV: Number of local governments (number)Target 1.a: Ensure significant mobilization of resources from a variety of sources, including through enhanced development cooperation, in order to provide adequate and predictable means for developing countries, in particular least developed countries, to implement programmes and policies to end poverty in all its dimensionsIndicator 1.a.1: Total official development assistance grants from all donors that focus on poverty reduction as a share of the recipient country’s gross national incomeDC_ODA_POVLG: Official development assistance grants for poverty reduction, by recipient countries (percentage of GNI)DC_ODA_POVDLG: Official development assistance grants for poverty reduction, by donor countries (percentage of GNI)DC_ODA_POVG: Official development assistance grants for poverty reduction (percentage of GNI)Indicator 1.a.2: Proportion of total government spending on essential services (education, health and social protection)SD_XPD_ESED: Proportion of total government spending on essential services, education (%)Target 1.b: Create sound policy frameworks at the national, regional and international levels, based on pro-poor and gender-sensitive development strategies, to support accelerated investment in poverty eradication actionsIndicator 1.b.1: Pro-poor public social spending

Explore comprehensive data on various indicators such as self-employment, female employment, average tariffs, net ODA provided, AIDS estimated deaths, fertility rate, school enrollment, GNI, gender parity index, agricultural support, poverty, and much more from the World Bank Millennium Development Goals dataset.

Self-employed, female (% of female employment), Average tariffs imposed by developed countries on agricultural products from developing countries (%), Net ODA provided to the least developed countries (% of donor GNI), AIDS estimated deaths (UNAIDS estimates), Fertility rate, total (births per woman), School enrollment, primary (% net), GNI, Atlas method (current US$), Average tariffs imposed by developed countries on clothing products from developing countries (%), School enrollment, primary (gross), gender parity index (GPI), Self-employed, total (% of total employment), Agricultural support estimate (% of GDP), Share of women in wage employment in the nonagricultural sector (% of total nonagricultural employment), Linear mixed-effect model estimates, Net ODA provided, total (current US$), School enrollment, secondary (gross), gender parity index (GPI), India, Bilateral, sector-allocable ODA to basic social services (% of bilateral ODA commitments), Average tariffs imposed by developed countries on clothing products from least developed countries (%), Bilateral ODA commitments that is untied (current US$), Qatar, Rural poverty gap at national poverty lines (%), GNI per capita, Atlas method (current US$), Urban poverty headcount ratio at national poverty lines (% of urban population), PPP conversion factor, private consumption (LCU per international $), Forest area (% of land area), Terrestrial protected areas (% of total land area), Poverty gap at national poverty lines (%), Annual, Proportion of seats held by women in national parliaments (%), Vulnerable employment, female (% of female employment), Contributing family workers, total (% of total employment), Net ODA provided, total (% of GNI), Total debt service (% of exports of goods, services and primary income), Total bilateral sector allocable ODA commitments (current US$), Average tariffs imposed by developed countries on textile products from least developed countries (%), Weighted Average, Net official development assistance received (current US$), Average tariffs imposed by developed countries on textile products from developing countries (%), Tuberculosis case detection rate (%, all forms), Oman, School enrollment, primary and secondary (gross), gender parity index (GPI), Prevalence of undernourishment (% of population), Population living in slums (% of urban population), Vulnerable employment, male (% of male employment), Debt service (PPG and IMF only, % of exports of goods, services and primary income), Ratio of school attendance rate of orphans to school attendance rate of non orphans, Weighted average, Net ODA received per capita (current US$), Population, total, Contributing family workers, male (% of male employment), Trade (% of GDP), Goods (excluding arms) admitted free of tariffs from least developed countries (% total merchandise imports excluding arms), Self-employed, male (% of male employment), PPP conversion factor, GDP (LCU per international $), Marine protected areas (% of territorial waters), Average tariffs imposed by developed countries on agricultural products from least developed countries (%), Pregnant women receiving prenatal care of at least four visits (% of pregnant women), Forest area (sq. km), Persistence to last grade of primary, total (% of cohort), Persistence to last grade of primary, female (% of cohort), Tuberculosis treatment success rate (% of new cases), Primary completion rate, total (% of relevant age group), School enrollment, tertiary (gross), gender parity index (GPI), Improved sanitation facilities (% of population with access), Poverty headcount ratio at national poverty lines (% of population), Net official development assistance and official aid received (current US$), Gross capital formation (% of GDP), Births attended by skilled health staff (% of total), Rural poverty headcount ratio at national poverty lines (% of rural population), Status under enhanced HIPC initiative, Children orphaned by HIV/AIDS, Vulnerable employment, total (% of total employment), Kuwait, Life expectancy at birth, total (years), Bahrain, Bilateral ODA commitments that is untied (% of bilateral ODA commitments), Persistence to last grade of primary, male (% of cohort), Bilateral, sector-allocable ODA to basic social services (current US$), Renewable internal freshwater resources per capita (cubic meters), Antiretroviral therapy coverage (% of people living with HIV), Pregnant women receiving prenatal care (%), Contributing family workers, female (% of female employment), Improved water source (% of population with access), Goods (excluding arms) admitted free of tariffs from developing countries (% total merchandise imports excluding arms), China, Total bilateral ODA commitments (current US$), Gap-filled total, Saudi Arabia, Adjusted net enrollment rate, primary (% of primary school age children), Reported cases of malaria, Annual freshwater withdrawals, total (% of internal resources), Net ODA received (% of GNI), Urban poverty gap at national poverty lines (%), Sum, Net ODA provided to the least developed countries (current US$), %

India, Qatar, Oman, Kuwait, Bahrain, China, Saudi Arabia

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The National Child Development Study (NCDS) is a continuing longitudinal study that seeks to follow the lives of all those living in Great Britain who were born in one particular week in 1958. The aim of the study is to improve understanding of the factors affecting human development over the whole lifespan.

The NCDS has its origins in the Perinatal Mortality Survey (PMS) (the original PMS study is held at the UK Data Archive under SN 2137). This study was sponsored by the National Birthday Trust Fund and designed to examine the social and obstetric factors associated with stillbirth and death in early infancy among the 17,000 children born in England, Scotland and Wales in that one week. Selected data from the PMS form NCDS sweep 0, held alongside NCDS sweeps 1-3, under SN 5565.

Survey and Biomeasures Data (GN 33004):
To date there have been ten attempts to trace all members of the birth cohort in order to monitor their physical, educational and social development. The first three sweeps were carried out by the National Children's Bureau, in 1965, when respondents were aged 7, in 1969, aged 11, and in 1974, aged 16 (these sweeps form NCDS1-3, held together with NCDS0 under SN 5565). The fourth sweep, also carried out by the National Children's Bureau, was conducted in 1981, when respondents were aged 23 (held under SN 5566). In 1985 the NCDS moved to the Social Statistics Research Unit (SSRU) - now known as the Centre for Longitudinal Studies (CLS). The fifth sweep was carried out in 1991, when respondents were aged 33 (held under SN 5567). For the sixth sweep, conducted in 1999-2000, when respondents were aged 42 (NCDS6, held under SN 5578), fieldwork was combined with the 1999-2000 wave of the 1970 Birth Cohort Study (BCS70), which was also conducted by CLS (and held under GN 33229). The seventh sweep was conducted in 2004-2005 when the respondents were aged 46 (held under SN 5579), the eighth sweep was conducted in 2008-2009 when respondents were aged 50 (held under SN 6137), the ninth sweep was conducted in 2013 when respondents were aged 55 (held under SN 7669), and the tenth sweep was conducted in 2020-24 when the respondents were aged 60-64 (held under SN 9412).

A Secure Access version of the NCDS is available under SN 9413, containing detailed sensitive variables not available under Safeguarded access (currently only sweep 10 data). Variables include uncommon health conditions (including age at diagnosis), full employment codes and income/finance details, and specific life circumstances (e.g. pregnancy details, year/age of emigration from GB).

Four separate datasets covering responses to NCDS over all sweeps are available. National Child Development Deaths Dataset: Special Licence Access (SN 7717) covers deaths; National Child Development Study Response and Outcomes Dataset (SN 5560) covers all other responses and outcomes; National Child Development Study: Partnership Histories (SN 6940) includes data on live-in relationships; and National Child Development Study: Activity Histories (SN 6942) covers work and non-work activities. Users are advised to order these studies alongside the other waves of NCDS.

From 2002-2004, a Biomedical Survey was completed and is available under End User Licence (EUL) (SN 8731) and Special Licence (SL) (SN 5594). Proteomics analyses of blood samples are available under SL SN 9254.

Linked Geographical Data (GN 33497):
A number of geographical variables are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies.

Linked Administrative Data (GN 33396):
A number of linked administrative datasets are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies. These include a Deaths dataset (SN 7717) available under SL and the Linked Health Administrative Datasets (SN 8697) available under Secure Access.

Multi-omics Data and Risk Scores Data (GN 33592)
Proteomics analyses were run on the blood samples collected from NCDS participants in 2002-2004 and are available under SL SN 9254. Metabolomics analyses were conducted on respondents of sweep 10 and are available under SL SN 9411.

Additional Sub-Studies (GN 33562):
In addition to the main NCDS sweeps, further studies have also been conducted on a range of subjects such as parent migration, unemployment, behavioural studies and respondent essays. The full list of NCDS studies available from the UK Data Service can be found on the NCDS series access data webpage.

How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from NCDS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Further information about the full NCDS series can be found on the Centre for Longitudinal Studies website.

SN 9412 - National Child Development Study: Age 62, Sweep 10, 2019-2024
The NCDS Age 62 Survey, (or 'Life in Your Early 60s' Survey as known to study members) was conducted between 2019 and 2024 when participants were aged 61-65 years. This sweep was designed and managed by the Centre for Longitudinal Studies (CLS) at the UCL Social Research Institute. Interviewer fieldwork was conducted by NatCen and Verian (formerly Kantar). Health visits were conducted by NatCen and INUVI. The Age 62 Survey involved an interview, a health visit, two paper self-completion questionnaires and an online dietary questionnaire.

The broad aim of the Age 62 Survey was to collect information which would aid the understanding of the lifelong factors affecting retirement and ageing. This survey also had a biomedical focus with physical measurements and assessments being conducted for the first time since the Age 44 biomedical sweep. The data collection built on the extensive data collected previously from birth and across the lifetime of study members and will facilitate comparisons with other generations as they reach the same life stage, allowing for study of social change.

The study was initially planned and designed to be conducted in-person. Fieldwork commenced in January 2020 but was subsequently paused in March 2020 due to the COVID-19 pandemic. As in-person interviewing was not feasible until early 2022, the protocol was adapted so that interviews could be conducted by video-call. Interviewer fieldwork restarted by video call in spring 2021 until April 2022 when it was feasible to return to in-person interviewing. The video mode option continued to be available if requested by a cohort member or was required due to interviewer capacity issues in a particular area.

Once mainstage fieldwork was complete, those who had not participated were invited to complete a short version of the questionnaire via web (known as the ‘mop-up’ survey). Cohort members who completed the survey between January-March 2020, were also invited to take part in the mop-up survey in order establish how their circumstances might have changed since the pandemic. Emigrants were not invited to take part in the main survey but were invited to take part in this short web-survey.

A full account of the survey development and fieldwork procedures can be found in the National Child Development Study technical report and appendices produced by NatCen Social Research, which accompanies this data.

The average for 2022 based on 47 countries was 74.51 years. The highest value was in Macao: 85.38 years and the lowest value was in Afghanistan: 62.88 years. The indicator is available from 1960 to 2022. Below is a chart for all countries where data are available.

Abstract

Disability and society: The last 20–30 years have seen an important change in our understanding of disability. From a previous individual perspective on causes and interventions, a social and civil rights approach has taken over. Much of the focus is now on the human and physical environment and how this might reduce or enhance an individual’s level of activity and social participation.

National policy development aimed at improving living conditions in general and among people with disabilities in particular is dependent on the availability of quality data. In many countries these have been lacking, and both the United Nations and National authorities have emphasised the need for this information in order to further develop disability policies.

Information about people with disabilities and their living conditions has the potential for contributing to an improvement of the situation faced by this group in many low-income countries, as has been demonstrated in high-income countries. The Studies on Living Conditions Among People with Activity Limitations in Developing Countries have been applied to inform policy development, for capacity building, awareness creation, and in specific advocacy processes to influence service delivery.

The studies have demonstrated that level of living conditions among disabled people is systematically lower than among non-disabled people. This implies that people with disabilities are denied the equal opportunities to participate and contribute to their society. It is in this context that people with disabilities are denied their human rights.

In Malawi, specific objectives were: - To develop a strategy and methodology for the collection of comprehensive, reliable and culturally adapted statistical data on living conditions among people with disabilities (with particular reference to the International Classification of Functioning, Disability and Health - ICF) - To carry out a representative National survey on the living conditions among persons with disabilities in Malawi so as to provide the much needed data for policy influence and planning - To lay the groundwork for future and long-term data collection among persons with disabilities in Malawi - To develop a collaboration in order to improve and strengthen research on the situation of people with disabilities in Southern Africa, and - To assist in capacity building among Disabled Persons Organisations (DPOs) in Malawi and among government ministries and other disability stakeholders to utilise the research findings.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Universe

The target population for sampling was all private households in Malawi excluding institutionalised and homeless people.

Kind of data

Sample survey data [ssd]

Sampling procedure

A two-stage cluster sampling procedure was applied using the National sampling frame in each country, in close collaboration with the National statistical offices who also did sample size calculations to ensure representativity at regional/provincial level. A required number of geographical units (often called Enumeration Areas, EAs) are thus sampled, with all households in these areas included in the first stage of the sampling. Then follows screening where all households in the selected areas are interviewed (normally the head of the household) using the WG 6 screening instrument.

Sampling in Malawi: The sample size was worked out noting that in a survey of living conditions of people with disabilities, the data user would want to know the estimates of proportions of respondents sharing respective views on issues relating to disability. The characteristics requiring respondents' views in this study are many and each characteristic would have its own proportion of respondents responding in a particular manner. In this regard, the proportion would vary from characteristic to characteristic. Determination of sample number of respondents that would give a national estimate of the proportion at a given level of precision depends on the variance of the proportion and the sample design adopted. A characteristic with a proportion having a large variance would require a larger sample to arrive at an estimate of the proportion at national level at a given acceptable level of precision than that with a smaller variance. In order to avoid having varying sample sizes for given characteristics of people with disabilities under the study, the largest possible sample number of people with disabilities based on the largest possible variance that a proportion can have at a given level of precision under given sample design was calculated. The variance of a proportion being highest when the proportion equals 50%, the required sample number of disabled persons was calculated based on the assumption that the estimated proportion would take that value with a margin of error equal to plus or minus 3.5 percent at the 95 percent level of confidence. Since the sample, as will be illustrated later, was to be drawn in stages, the design effect was assumed to be equal to 2. The design effect is the effect on the variance of adopting a sampling procedure other than Simple Random Sampling (Bradley and South, 1981).The national sample size derived was made up of 1570 respondents.

The sampling frame that was utilized in this survey was obtained from the National Statistical Office (NSO). This frame was developed by NSO through the operations of the most recent population Census in Malawi conducted in 1998. Through a mapping exercise prior to the census, a total of 9206 Enumeration Areas were demarcated covering the whole country. The boundaries of these areas followed physical features such as rivers/streams, roads/paths, galleys, etc. and these enumeration areas were demarcated in such a way that during the census an enumerator would enumerate all the persons in a given enumeration area within maximum of 21 days. Each enumeration area is estimated to have approximately 300 households or an estimated 1,000 individuals. During the operations of the census, the number of persons as well as the number of households found to exist in each one of the enumeration areas was recorded. However, no list of names and location of the households within the respective enumeration areas were made. This was due to the problems which are inherent in Malawi as well as most developing countries in giving information leading to the location of a household especially in the rural areas. Malawi has a total of 28 Districts divided into Traditional Authorities (TAs). In rural areas, the Traditional Authority is the lowest units for which maps showing boundaries of the enumeration areas are available while in the cities areas called Wards are the lowest unit for which enumeration area maps are available.

Iit was calculated that a sample of 1570 persons with disabilities would be adequate to provide estimates of acceptable precision at the national level and the terms of reference dictated that there should be complete enumeration of all the people with disabilities in the sampled enumeration areas. The lowest level for which the available frame had information, as discussed above, was the enumeration area and the information comprised of only totals of persons and households. In addition, there was no information on the prevalence of persons with disabilities at the enumeration area level.

The study conducted by SINTEF Health Research and the University of Zimbabwe using the ICF definition of disability (Eide, Nhiwatiwa, Muderezi & Loeb, 2004) estimated the proportion of those disabled to be 1.9%; while the one conducted in Namibia (Eide, van Rooy & Loeb, 2003) estimated proportion of disabled in that country to be 1.6%. Lessons learnt from Namibia and Zimbabwe indicate, therefore, that utilizing the ICF definition, the prevalence of disabled persons in Malawi may be closer to the 2.9% estimate of 1983 (NSO, 1987). In the absence of information on the prevalence of disabled persons in Malawi at enumeration area level, it was assumed that the prevalence of disabled persons in each enumeration area would be 3%. Hence, in order to be able to sample and budget for the study, it was assumed that an enumeration area would contain on average 3% of its total number of households having at least a member with a disability. Based on this assumption and considering an average of approximately 300 households per enumeration area, it was calculated that the household with at least one disabled person would on average equal to 10 in an enumeration area. Considering the coverage of 1570 disabled persons, and that an enumeration area would contain on average 10 households with at least one disabled member, a sample of 157 enumeration areas were planned to be covered in the study within which all persons identified to have a disability were to be interviewed.

Each one of the districts (Likoma Island was excluded for logistical reasons) as well as each of the three cities in Malawi formed a stratum. The total sample of 157 enumeration areas was allocated to the respective strata in proportion to the population of the stratum and the distribution thereof. The selection of the allocated number of enumeration areas within each stratum was done with probability proportional to size prior to the commencement of the data collection exercise. The size measure was the human population of the enumeration areas as found in the 1998 population census.

Apart from enumerating all households having at least a person with a disability in a selected enumeration area (Cases) a similar number of households (designated as minimum 10 per enumeration area) without any disabled persons (Controls) should also be

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population. The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population. The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

Monaco had the highest life expectancy among both men and women worldwide as of 2024. That year, life expectancy for men and women was ** and ** years, respectively. The East Asian countries and regions, Hong Kong, Japan, South Korea, and Macao, followed. Many of the countries on the list are struggling with aging populations and a declining workforce as more people enter retirement age compared to people entering employment.