A global phenomenon, known as the demographic transition, has seen life expectancy from birth increase rapidly over the past two centuries. In pre-industrial societies, the average life expectancy was around 24 years, and it is believed that this was the case throughout most of history, and in all regions. The demographic transition then began in the industrial societies of Europe, North America, and the West Pacific around the turn of the 19th century, and life expectancy rose accordingly. Latin America was the next region to follow, before Africa and most Asian populations saw their life expectancy rise throughout the 20th century.

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.

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.

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.

Analysis of ‘Life Expectancy (WHO)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/kumarajarshi/life-expectancy-who on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Although there have been lot of studies undertaken in the past on factors affecting life expectancy considering demographic variables, income composition and mortality rates. It was found that affect of immunization and human development index was not taken into account in the past. Also, some of the past research was done considering multiple linear regression based on data set of one year for all the countries. Hence, this gives motivation to resolve both the factors stated previously by formulating a regression model based on mixed effects model and multiple linear regression while considering data from a period of 2000 to 2015 for all the countries. Important immunization like Hepatitis B, Polio and Diphtheria will also be considered. In a nutshell, this study will focus on immunization factors, mortality factors, economic factors, social factors and other health related factors as well. Since the observations this dataset are based on different countries, it will be easier for a country to determine the predicting factor which is contributing to lower value of life expectancy. This will help in suggesting a country which area should be given importance in order to efficiently improve the life expectancy of its population.

Content

The project relies on accuracy of data. The Global Health Observatory (GHO) data repository under World Health Organization (WHO) keeps track of the health status as well as many other related factors for all countries The data-sets are made available to public for the purpose of health data analysis. The data-set related to life expectancy, health factors for 193 countries has been collected from the same WHO data repository website and its corresponding economic data was collected from United Nation website. Among all categories of health-related factors only those critical factors were chosen which are more representative. It has been observed that in the past 15 years , there has been a huge development in health sector resulting in improvement of human mortality rates especially in the developing nations in comparison to the past 30 years. Therefore, in this project we have considered data from year 2000-2015 for 193 countries for further analysis. The individual data files have been merged together into a single data-set. On initial visual inspection of the data showed some missing values. As the data-sets were from WHO, we found no evident errors. Missing data was handled in R software by using Missmap command. The result indicated that most of the missing data was for population, Hepatitis B and GDP. The missing data were from less known countries like Vanuatu, Tonga, Togo, Cabo Verde etc. Finding all data for these countries was difficult and hence, it was decided that we exclude these countries from the final model data-set. The final merged file(final dataset) consists of 22 Columns and 2938 rows which meant 20 predicting variables. All predicting variables was then divided into several broad categories:​Immunization related factors, Mortality factors, Economical factors and Social factors.

Acknowledgements

The data was collected from WHO and United Nations website with the help of Deeksha Russell and Duan Wang.

Inspiration

The data-set aims to answer the following key questions: 1. Does various predicting factors which has been chosen initially really affect the Life expectancy? What are the predicting variables actually affecting the life expectancy? 2. Should a country having a lower life expectancy value(<65) increase its healthcare expenditure in order to improve its average lifespan? 3. How does Infant and Adult mortality rates affect life expectancy? 4. Does Life Expectancy has positive or negative correlation with eating habits, lifestyle, exercise, smoking, drinking alcohol etc. 5. What is the impact of schooling on the lifespan of humans? 6. Does Life Expectancy have positive or negative relationship with drinking alcohol? 7. Do densely populated countries tend to have lower life expectancy? 8. What is the impact of Immunization coverage on life Expectancy?

--- Original source retains full ownership of the source dataset ---

BackgroundDecades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic.MethodsWe used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy.FindingsAll countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy across all countries (overall mean –0·18 years [95% UI –0·22 to –0·13]), with all countries having an absolute fall in life expectancy except for Ireland, Iceland, Sweden, Norway, and Denmark, which showed marginal improvement in life expectancy, and Belgium, which showed no change in life expectancy. Across countries, the causes of death responsible for the largest improvements in life expectancy from 1990 to 2011 were cardiovascular diseases and neoplasms. Deaths from cardiovascular diseases were the primary driver of reductions in life expectancy improvements during 2011–19, and deaths from respiratory infections and other COVID-19 pandemic-related outcomes were responsible for the decreases in life expectancy during 2019–21. Deaths from cardiovascular diseases and neoplasms in 2019 were attributable to high systolic blood pressure, dietary risks, tobacco smoke, high LDL cholesterol, high BMI, occupational risks, high alcohol use, and other risks including low physical activity. Exposure to these major risk factors differed by country, with trends of increasing exposure to high BMI and decreasing exposure to tobacco smoke observed in all countries during 1990–2021.InterpretationThe countries that best maintained improvements in life expectancy after 2011 (Norway, Iceland, Belgium, Denmark, and Sweden) did so through better maintenance of reductions in mortality from cardiovascular diseases and neoplasms, underpinned by decreased exposures to major risks, possibly mitigated by government policies. The continued improvements in life expectancy in five countries during 2019–21 indicate that these countries were better prepared to withstand the COVID-19 pandemic. By contrast, countries with the greatest slowdown in life expectancy improvements after 2011 went on to have some of the largest decreases in life expectancy in 2019–21. These findings suggest that government policies that improve population health also build resilience to future shocks. Such policies include reducing population exposure to major upstream risks for cardiovascular diseases and neoplasms, such as harmful diets and low physical activity, tackling the commercial determinants of poor health, and ensuring access to affordable health services.

Global life expectancy at birth has risen significantly since the mid-1900s, from roughly 46 years in 1950 to 73.2 years in 2023. Post-COVID-19 projections There was a drop of 1.7 years during the COVID-19 pandemic, between 2019 and 2021, however, figures resumed upon their previous trajectory the following year due to the implementation of vaccination campaigns and the lower severity of later strains of the virus. By the end of the century it is believed that global life expectancy from birth will reach 82 years, although growth will slow in the coming decades as many of the more-populous Asian countries reach demographic maturity. However, there is still expected to be a wide gap between various regions at the end of the 2100s, with the Europe and North America expected to have life expectancies around 90 years, whereas Sub-Saharan Africa is predicted to be in the low-70s. The Great Leap Forward While a decrease of one year during the COVID-19 pandemic may appear insignificant, this is the largest decline in life expectancy since the "Great Leap Forward" in China in 1958, which caused global life expectancy to fall by almost four years between by 1960. The "Great Leap Forward" was a series of modernizing reforms, which sought to rapidly transition China's agrarian economy into an industrial economy, but mismanagement led to tens of millions of deaths through famine and disease.

Bermuda: Life expectancy, in years: The latest value from 2022 is 81.57 years, an increase from 79.28 years in 2021. In comparison, the world average is 72.24 years, based on data from 192 countries. Historically, the average for Bermuda from 1960 to 2022 is 74.98 years. The minimum value, 63.43 years, was reached in 1966 while the maximum of 82.35 years was recorded in 2017.

In 2022, India overtook China as the world's most populous country and now has almost 1.46 billion people. China now has the second-largest population in the world, still with just over 1.4 billion inhabitants, however, its population went into decline in 2023. Global population As of 2025, the world's population stands at almost 8.2 billion people and is expected to reach around 10.3 billion people in the 2080s, when it will then go into decline. Due to improved healthcare, sanitation, and general living conditions, the global population continues to increase; mortality rates (particularly among infants and children) are decreasing and the median age of the world population has steadily increased for decades. As for the average life expectancy in industrial and developing countries, the gap has narrowed significantly since the mid-20th century. Asia is the most populous continent on Earth; 11 of the 20 largest countries are located there. It leads the ranking of the global population by continent by far, reporting four times as many inhabitants as Africa. The Demographic Transition The population explosion over the past two centuries is part of a phenomenon known as the demographic transition. Simply put, this transition results from a drastic reduction in mortality, which then leads to a reduction in fertility, and increase in life expectancy; this interim period where death rates are low and birth rates are high is where this population explosion occurs, and population growth can remain high as the population ages. In today's most-developed countries, the transition generally began with industrialization in the 1800s, and growth has now stabilized as birth and mortality rates have re-balanced. Across less-developed countries, the stage of this transition varies; for example, China is at a later stage than India, which accounts for the change in which country is more populous - understanding the demographic transition can help understand the reason why China's population is now going into decline. The least-developed region is Sub-Saharan Africa, where fertility rates remain close to pre-industrial levels in some countries. As these countries transition, they will undergo significant rates of population growth

Data for Figures and Tables in "Bounce backs amid continued losses: Life expectancy changes since COVID-19"

cc-by Jonas Schöley, José Manuel Aburto, Ilya Kashnitsky, Maxi S. Kniffka, Luyin Zhang, Hannaliis Jaadla, Jennifer B. Dowd, and Ridhi Kashyap. "Bounce backs amid continued losses: Life expectancy changes since COVID-19".

These are CSV files of data in the figures and tables published in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19".

50-e0diffT.csv

Figure 1: Life expectancy changes 2019/20 and 2020/21 across countries. The countries are ordered by increasing cumulative life expectancy losses since 2019. Grey dots indicate the average annual LE changes over the years 2015 through 2019.

51-arriagaT.csv

Figure 2: Age contributions to life expectancy changes since 2019 separated for 2020 and 2021. The position of the arrowhead indicates the total contribution of mortality changes in a given age group to the change in life expectancy at birth since 2019. The discontinuity in the arrow indicates those contributions separately for the years 2020 and 2021. Annual contributions can compound or reverse. The total life expectancy change from 2019 to 2021 in a given country is the sum of the arrowhead positions across age.

52-sexdiff.csv

Figure 3: Change in the female life expectancy advantage from 2019 through 2021. Blue colors indicate an increase and red colors a decrease in the female life expectancy advantage. Muted colors indicate non-significant changes.

53-e0diffcodT.csv

Figure 4: Life expectancy deficit in 2021 decomposed into contributions by age and cause of death. LE deficit is defined as observed minus expected life expectancy had pre-pandemic mortality trends continued.

55-vaxe0.csv

Figure 5: Years of life expectancy deficit during October through December 2021 contributed by ages <60 and 60+ against % of population twice vaccinated by October 1st in the respective age groups. LE deficit is defined as the counterfactual LE from a Lee-Carter mortality forecast based on death rates for the fourth quarter of the years 2015 to 2019 minus observed LE.

54-tab_arriaga.csv

Table 1: Months of life expectancy (LE) changes and deficits (labelled ES) since the start of the pandemic attributed to age-specific mortality changes (labelled AT). LE deficit is defined as observed minus expected life expectancy had pre-pandemic mortality trends continued.

Portugal life expectancy for 2025 is 82.95, a 0.18% increase from 2024.

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

<li>Portugal life expectancy for 2024 was <strong>82.80</strong>, a <strong>0.63% increase</strong> from 2023.</li>
<li>Portugal life expectancy for 2023 was <strong>82.28</strong>, a <strong>0.79% increase</strong> from 2022.</li>
<li>Portugal life expectancy for 2022 was <strong>81.63</strong>, a <strong>0.31% 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.

Abstract

The Health Inequality Project uses big data to measure differences in life expectancy by income across areas and identify strategies to improve health outcomes for low-income Americans.

Section 7

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution. Both race-adjusted and unadjusted estimates are reported.

Source

Section 13

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution separately by year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 6

This dataset was created on 2020-01-10 18:53:00.508 by merging multiple datasets together. The source datasets for this version were:

Commuting Zone Life Expectancy Estimates by year: CZ-level by-year life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy: Commuting zone (CZ)-level life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy Trends: CZ-level estimates of trends in life expectancy for men and women, by income quartile

Commuting Zone Characteristics: CZ-level characteristics

Commuting Zone Life Expectancy for larger populations: CZ-level life expectancy estimates for men and women, by income ventile

Section 15

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by state of residence and year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 11

This table reports US mortality rates by gender, age, year and household income percentile. Household incomes are measured two years prior to the mortality rate for mortality rates at ages 40-63, and at age 61 for mortality rates at ages 64-76. The “lag” variable indicates the number of years between measurement of income and mortality.

Observations with 1 or 2 deaths have been masked: all mortality rates that reflect only 1 or 2 deaths have been recoded to reflect 3 deaths

Source

Section 3

This table reports coefficients and standard errors from regressions of life expectancy estimates for men and women at age 40 for each quartile of the national income distribution on calendar year by commuting zone of residence. Only the slope coefficient, representing the average increase or decrease in life expectancy per year, is reported. Trend estimates for both race-adjusted and unadjusted life expectancies are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 9

This table reports life expectancy estimates at age 40 for Males and Females for all countries. Source: World Health Organization, accessed at: http://apps.who.int/gho/athena/

Source

Section 10

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by county of residence. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for counties with populations larger than 25,000 only

Source

Section 2

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by commuting zone of residence and year. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 8

This table reports US population and death counts by age, year, and sex from various sources. Counts labelled “dm1” are derived from the Social Security Administration Data Master 1 file. Counts labelled “irs” are derived from tax data. Counts labelled “cdc” are derived from NCHS life tables.

Source

Section 12

This table reports numerous county characteristics, compiled from various sources. These characteristics are described in the county life expectancy table.

Two variables constructed by the Cen

This statistic shows the average life expectancy in Europe for those born in 2024, by gender and region. The average life expectancy in Western Europe was 79 years for males and 84 years for females in 2024. Additional information on European life expectancy The difference in life expectancy seen between men and women across all European regions is in line with the global trends of women outliving men, on average. The average life expectancy at birth worldwide by income group shows that the gender life expectancy gap is not only a consistent trend across countries, but also income groups. Moreover, the higher life expectancy for those in high income groups may help to explain the lower average life expectancy for those born in Eastern Europe where average incomes are generally lower than other European regions. Although income and length of life are not directly correlated, higher income individuals are generally able to afford access to superior nutrition and healthcare as well as having leisure time for exercise. That said, current trends in the increases in life expectancy worldwide by country between 1970 and 2017 suggest economic growth will lead to larger increases in life expectancy. Those increases are less likely to occur to such a degree in the more developed regions of Europe where Italy, Spain, France, Switzerland, Iceland and Austria all rank in the top 20 countries with the highest life expectancy.

In 2025, the average age in the Philippines is expected to reach 26.1 years, increasing to roughly 46.1 years of age by 2100. This is a significant rise, considering that until the year 2000, the country’s median age was under 20 years old. From 2011 to 2021, the share of very young people decreased, while the age brackets for people aged 15-64 and 65 or older grew. This shift in age structure implies a lower birth rate, as well as an aging population. Birth and family size As of 2020, the birth rate in the Philippines is just under 22 children born per thousand inhabitants each year, about 3 less than in the decade before. The fertility rate has likewise been decreasing since 2007, but is still higher than the Oceania region’s average as of 2020. Fewer newborns each year contributes to a lower median age. High mortality in the Philippines is preventable Life expectancy is also factor in a rising median age, although increasing only marginally in the Philippines compared with neighboring countries Cambodia, Myanmar, and Laos (but still higher than in these countries). The life expectancy in the Philippines was just under 72 years of age in 2017, and roughly three years shorter than in Thailand or Vietnam. One factor that lowers the life expectancy is the high mortality rate due to noncontagious diseases, such as cancer and heart and respiratory problems, accounting for more than a quarter of early deaths from ages 30 to 70 in the Philippines.

Background: Mauritius along with other 12 countries in the African Region was identified at the early start of the COVID-19 pandemic as being at high risk due to high volume of international travel, high prevalence of non-communicable diseases and co-morbidities, high population density and significant share of population over 60 years (16%). The objective of this study was to estimate the total discounted money value of human life losses (TDMVCLMAURITIUS) associated with COVID-19 in Mauritius.Methods: The human capital approach (HCA) was used to estimate the TDMVCLMAURITIUS of the 10 human life losses linked with COVID-19 in Mauritius as of 16 October 2020. The HCA model was estimated with the national life expectancy of 75.51 years and a discount rate of 3%. A sensitivity analysis was performed assuming (a) 5 and 10% discount rates, and (b) the average world life expectancy of 73.2 years, and the world highest life expectancy of 88.17 years.Results: The money value of human lives lost to COVID-19, at a discounted rate of 3%, had an estimated TDMVCLMAURITIUS of Int$ 3,120,689, and an average of Int$ 312,069 per human life lost. Approximately 74% of the TDMVCLMAURITIUS accrued to persons aged between 20 and 59 years. Reanalysis of the model with 5 and 10% discount rates, holding national life expectancy constant, reduced the TDMVCLMAURITIUS by 19.0 and 45.5%, respectively. Application of the average world life expectancy at 3% discount rate reduced TDMVCLMAURITIUS by 13%; and use of the world highest life expectancy at 3% discount rate increased TDMVCLMAURITIUS by 50%.Conclusions: The average discounted money value per human life loss associated with COVID-19 is 12-fold the per capita GDP for Mauritius. All measures implemented to prevent widespread community transmission of COVID-19 may have saved the country 837 human lives worth Int$258,080,991. This evidence, conjointly with human rights arguments, calls for increased investments to bridge the existing gaps for achieving universal health coverage by 2030.

Georgia life expectancy for 2025 is 74.53, a 0.19% increase from 2024.

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

<li>Georgia life expectancy for 2024 was <strong>74.38</strong>, a <strong>0.19% increase</strong> from 2023.</li>
<li>Georgia life expectancy for 2023 was <strong>74.24</strong>, a <strong>0.19% increase</strong> from 2022.</li>
<li>Georgia life expectancy for 2022 was <strong>74.10</strong>, a <strong>0.19% 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.

Between 1993 and 2020, India’s life expectancy increased significantly in every state for both men and women. The life expectancy at the national level increased from 61.1 years in 1993-97 to 70 years in 2016-20, representing a significant gain of almost 9 years. In addition, a notable gender disparity in life expectancy was documented during this period, with women generally outliving men in most of the Indian states. During this period the gender difference in life expectancy grew from 1.4 years in 1993–1997 to 2.8 years in 2016–20. Kerala consistently maintained the highest life expectancy in the country, reaching 75 years in 2016-20, significantly above the national average.

Objective Gains in life expectancy have faltered in several high-income countries in recent years. We aim to compare life expectancy trends in Scotland to those seen internationally, and to assess the timing of any recent changes in mortality trends for Scotland. Setting Austria, Croatia, Czech Republic, Denmark, England & Wales, Estonia, France, Germany, Hungary, Iceland, Israel, Japan, Korea, Latvia, Lithuania, Netherlands, Northern Ireland, Poland, Scotland, Slovakia, Spain, Sweden, Switzerland, USA. Methods We used life expectancy data from the Human Mortality Database (HMD) to calculate the mean annual life expectancy change for 24 high-income countries over five-year periods from 1992 to 2016, and the change for Scotland for five-year periods from 1857 to 2016. One- and two-break segmented regression models were applied to mortality data from National Records of Scotland (NRS) to identify turning points in age-standardised mortality trends between 1990 and 2018. Results In 2012-2016 life expectancies in Scotland increased by 2.5 weeks/year for females and 4.5 weeks/year for males, the smallest gains of any period since the early 1970s. The improvements in life expectancy in 2012-2016 were smallest among females (<2.0 weeks/year) in Northern Ireland, Iceland, England & Wales and the USA and among males (<5.0 weeks/year) in Iceland, USA, England & Wales and Scotland. Japan, Korea, and countries of Eastern Europe have seen substantial gains in the same period. The best estimate of when mortality rates changed to a slower rate of improvement in Scotland was the year to 2012 Q4 for males and the year to 2014 Q2 for females. Conclusion Life expectancy improvement has stalled across many, but not all, high income countries. The recent change in the mortality trend in Scotland occurred within the period 2012-2014. Further research is required to understand these trends, but governments must also take timely action on plausible contributors. Methods Description of methods used for collection/generation of data: The HMD has a detailed methods protocol available here: https://www.mortality.org/Public/Docs/MethodsProtocol.pdf The ONS and NRS also have similar methods for ensuring data consistency and quality assurance.

Methods for processing the data: The segmented regression was conducted using the 'segmented' package in R. The recommended references to this package and its approach are here: Vito M. R. Muggeo (2003). Estimating regression models with unknown break-points. Statistics in Medicine, 22, 3055-3071.

Vito M. R. Muggeo (2008). segmented: an R Package to Fit Regression Models with Broken-Line Relationships. R News, 8/1, 20-25. URL https://cran.r-project.org/doc/Rnews/.

Vito M. R. Muggeo (2016). Testing with a nuisance parameter present only under the alternative: a score-based approach with application to segmented modelling. J of Statistical Computation and Simulation, 86, 3059-3067.

Vito M. R. Muggeo (2017). Interval estimation for the breakpoint in segmented regression: a smoothed score-based approach. Australian & New Zealand Journal of Statistics, 59, 311-322.

Software- or Instrument-specific information needed to interpret the data, including software and hardware version numbers: The analyses were conducted in R version 3.6.1 and Microsoft Excel 2013.

Please see README.txt for further information

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.

Singapore had the highest life expectancy at birth of all the Southeast Asian countries in 2023, with its citizens expected to live to an average of **** years. Falling behind by almost 20 years was Myanmar, with a life expectancy of **** years old at birth as of 2023. Interestingly, Singapore made the top ten of countries with the highest average life expectancy worldwide. Increasing life expectancyLife expectancy throughout the Southeast Asian region has been rising throughout recent years, likely due to improved healthcare systems. Improvements brought about by increasing healthcare expenditures. The East Asian region also joined Southeast Asia in displaying higher life expectancies at birth, with Hong Kong and Macao all exhibiting life expectancies at birth of over 85 years old. Improved healthcare Thailand, the Philippines and Singapore are just some of the Southeast Asian governments which have released successful universal healthcare plans. As the region faces an aging population, there has been more demand for effective healthcare. Healthcare has been improving not just in the Southeast Asian region but throughout the whole Asia Pacific region, with many countries exhibiting near perfect child immunization rates, offering its citizens better healthcare from birth. With these improvements made, it does not seem surprising that life expectancy at birth has increased.