This map displays data from the Selected Social and Economic Indicators dataset (tables DP02 and DP03) from the American Community Survey 5-Yr Estimates, U.S. Census Bureau. Economic and education measures are from 2010, while race/ethnicity estimates are from 2011, these data are presented at the census tract level. Life expectancy is presented at the small area level, as defined by NMDOH, and is based on birth/mortality records for the period 2007-2011.

In 2023, the average life expectancy for those born in more developed countries was 75 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 63 and 67 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.

A dataset of a longitudinal study of over 3,000 Mexican-Americans aged 65 or over living in five southwestern states. The objective is to describe the physical and mental health of the study group and link them to key social variables (e.g., social support, health behavior, acculturation, migration). To the extent possible, the study was modeled after the existing EPESE studies, especially the Duke EPESE, which included a large sample if African-Americans. Unlike the other EPESE studies that were restricted to small geographic areas, the Hispanic EPESE aimed at obtaining a representative sample of community-dwelling Mexican-American elderly residing in Texas, New Mexico, Arizona, Colorado, and California. Approximately 85% of Mexican-American elderly reside in these states and data were obtained that are generalizable to roughly 500,000 older people. The final sample of 3,050 subjects at baseline is comparable to those of the other EPESE studies. Data Availability: Waves I to IV are available through the National Archive of Computerized Data on Aging (NACDA), ICPSR. Also available through NACDA is the ����??Resource Book of the Hispanic Established Populations for the Epidemiologic Studies of the Elderly����?? which offers a thorough review of the data and its applications. All subjects aged 75 or older were interviewed for Wave V and 902 new subjects were added. Hemoglobin A1c test kits were provided to subjects who self-reported diabetes. Approximately 270 of the kits were returned for analyses. Wave V data are being validated and reviewed. A tentative timeline for the archiving of Wave V data is November 2006. Wave VI interviewing and data collection is scheduled to begin in Fall 2006. * Dates of Study: 1993-2006 * Study Features: Longitudinal, Minority oversamples, Anthropometric Measures * Sample Size: ** 1993-4: 3,050 (Wave I) ** 1995-6: 2,438 (Wave II) ** 1998-9: 1,980 (Wave III) ** 2000-1: 1,682 (Wave IV) ** 2004-5: 2,073 (Wave V) ** 2006-7: (Wave VI) Links: * ICPSR Wave 1: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/2851 * ICPSR Wave 2: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/3385 * ICPSR Wave 3: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/4102 * ICPSR Wave 4: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/4314 * ICPSR Wave 5: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/25041 * ICPSR Wave 6: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/29654

Countries from Natural Earth 50M scale data with a Human Development Index attribute for each of the following years: 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2013, 2015, & 2017. The Human Development Index measures achievement in 3 areas of human development: long life, good education and income. Specifically, the index is computed using life expectancy at birth, Mean years of schooling, expected years of schooling, and gross national income (GNI) per capita (PPP $). The United Nations categorizes the HDI values into 4 groups. In 2013 these groups were defined by the following HDI values: Very High: 0.736 and higher High: 0.615 to 0.735 Medium: 0.494 to 0.614 Low: 0.493 and lower

In 2015 & 2017 these groups were defined by the following HDI values: Very High: 0.800 and higher High: 0.700 to 0.799 Medium: 0.550 to 0.699 Low: 0.549 and lower

Human Development Index attributes are from The World Bank: HDRO calculations based on data from UNDESA (2013a), Barro and Lee (2013), UNESCO Institute for Statistics (2013), UN Statistics Division(2014), World Bank (2014) and IMF (2014). 2015 & 2017 values source: HDRO calculations based on data from UNDESA (2017a), UNESCO Institute for Statistics (2018), United Nations Statistics Division (2018b), World Bank (2018b), Barro and Lee (2016) and IMF (2018).

Population data are from (1) United Nations Population Division. World Population Prospects, (2) United Nations Statistical Division. Population and Vital Statistics Report (various years), (3) Census reports and other statistical publications from national statistical offices, (4) Eurostat: Demographic Statistics, (5) Secretariat of the Pacific Community: Statistics and Demography Programme, and (6) U.S. Census Bureau: International Database.

This paper exploits an unusual transportation setting to generate some of the first revealed preference value of a statistical life (VSL) estimates from a low-income setting. We estimate the trade-offs individuals are willing to make between mortality risk and cost as they travel to and from the international airport in Sierra Leone (which is separated from the capital Freetown by a body of water). We observe travelers choosing among multiple transport options – namely, ferry, helicopter, hovercraft, and water taxi. The setting and original dataset allow us to address some typical omitted variable concerns, and also to compare VSL estimates for travelers from dozens of countries, including both African and non-African countries, all facing the same choice situation. The average VSL estimate for African travelers in the sample is US$577,000 compared to US$924,000 for non-Africans. Individual characteristics, particularly job earnings and fatalistic attitudes, can largely account for this variation in the estimated VSL, but there is little evidence that estimates are driven by individuals’ lack of information or predicted life expectancy. We estimate a large income elasticity of the VSL of +1.77. These VSL estimates begin to fill an important gap in the existing literature, and can be used to inform public policy, including current debates within Sierra Leone regarding the desirability of constructing new transportation infrastructure.

Climate change, urbanization, and global trade have contributed to the recent spread of dengue viruses. In this study, we investigate the relationship between dengue occurrence in humans, climate factors (temperature and minimum quarterly rainfall), socio-economic factors (such as household income, regional rates of education, regional unemployment, housing overcrowding, life expectancy, and medical resources), and demographic factors (such as migration flows, age structure of the population, and population density). From a geographical perspective, this study focuses on Mexico and parts of the United States to exploit similarity in climate conditions and differences in socio-economic and demographic factors, so as to try to isolate the role of the latter. Areas at risk of dengue are first selected based on the predicted presence of at least one of the two mosquito vectors responsible for dengue's transmission: Aedes aegypti and Aedes albopictus. The presence of the mosquito in a region...