Throughout most of history, average life expectancy from birth was fairly consistent across the globe, at around 24 years. A major contributor to this was high rates of infant and child mortality; those who survived into adulthood could expect to live to their 50s or 60s, yet pandemics, food instability, and conflict did cause regular spikes in mortality across the entire population. Gradually, from the 16th to 19th centuries, there was some growth in more developed societies, due to improvements in agriculture, infrastructure, and medical knowledge. However, the most significant change came with the introduction of vaccination and other medical advances in the 1800s, which saw a sharp decline in child mortality and the onset of the demographic transition. This phenomenon began in more developed countries in the 1800s, before spreading to Latin America, Asia, and (later) Africa in the 1900s. As the majority of the world's population lives in countries considered to be "less developed", this figure is much closer to the global average. However, today, there is a considerable difference in life expectancies across these countries, ranging from 84.7 years in Japan to 53 years in the Central African Republic.

For most of the world, throughout most of human history, the average life expectancy from birth was around 24. This figure fluctuated greatly depending on the time or region, and was higher than 24 in most individual years, but factors such as pandemics, famines, and conflicts caused regular spikes in mortality and reduced life expectancy. Child mortality The most significant difference between historical mortality rates and modern figures is that child and infant mortality was so high in pre-industrial times; before the introduction of vaccination, water treatment, and other medical knowledge or technologies, women would have around seven children throughout their lifetime, but around half of these would not make it to adulthood. Accurate, historical figures for infant mortality are difficult to ascertain, as it was so prevalent, it took place in the home, and was rarely recorded in censuses; however, figures from this source suggest that the rate was around 300 deaths per 1,000 live births in some years, meaning that almost one in three infants did not make it to their first birthday in certain periods. For those who survived to adolescence, they could expect to live into their forties or fifties on average. Modern figures It was not until the eradication of plague and improvements in housing and infrastructure in recent centuries where life expectancy began to rise in some parts of Europe, before industrialization and medical advances led to the onset of the demographic transition across the world. Today, global life expectancy from birth is roughly three times higher than in pre-industrial times, at almost 73 years. It is higher still in more demographically and economically developed countries; life expectancy is over 82 years in the three European countries shown, and over 84 in Japan. For the least developed countries, mostly found in Sub-Saharan Africa, life expectancy from birth can be as low as 53 years.

File Description: "Life Expectancy Data.csv" This dataset contains 2,938 entries and 22 columns, covering life expectancy and related health indicators for multiple nations from 2000 to 2015. It includes country-wise data and other economic, social, and health metrics. Column Description: 1. Country – Name of the country. 2. Year – Data year (ranging from 2000 to 2015). 3. Status – Economic classification (Developing/Developed). 4. Life expectancy – Average lifespan in years. 5. Adult Mortality – Probability of death between ages 15-60 per 1,000 individuals. 6. Infant Deaths – Number of infant deaths per 1,000 live births. 7. Alcohol – Per capita alcohol consumption. 8. Percentage Expenditure – Government health expenditure as a percentage of GDP. 9. Hepatitis B – Immunization coverage percentage. 10. Measles – Number of reported measles cases. 11. BMI – Average Body Mass Index. 12. Under-Five Deaths – Mortality rate for children under five. 13. Polio & Diphtheria – Immunization rates. 14. HIV/AIDS – Deaths due to HIV/AIDS per 1,000 individuals. 15. GDP – Gross Domestic Product per capita. 16. Population – Total population of the country. 17. Thinness (1-19 years, 5-9 years) – Percentage of underweight children. 18. Income Composition of Resources– Human development index proxy. 19. Schooling– Average number of years of schooling. Missing Data: Some columns (like Hepatitis B, GDP, Population, Total Expenditure) contain missing values. Further File Information: Total Countries: 193 Years Covered: 2000–2015 Total Entries: 2,938 Missing Data Overview: Some columns have missing values, notably: Hepatitis B (553 missing) GDP (448 missing) Population (652 missing) Total expenditure (226 missing) Income Composition of Resources (167 missing) Schooling (163 missing) Summary Statistics: Life Expectancy:

Range: 36.3 to 89 years Mean: 69.2 years Adult Mortality:

Mean: 165 per 1,000 Max: 723 per 1,000 GDP per Capita:

Mean: $7,483 Max: $119,172 Population:

Mean: ~12.75 million Max: 1.29 billion Education:

Schooling Average: 12 years Max: 20.7 years

Futuristic Scope of this data: For comparative analysis of the 2000–2015 life expectancy dataset with new datasets on the same parametres , you can perform several statistical tests and analytical methods based on different research questions. Below are some key tests and approaches:

1. Trend Analysis (Time-Series) Objective: Identify trends in life expectancy and related indicators over time. Methods: Moving Averages: Smooth fluctuations to detect trends. Linear/Polynomial Regression: Check whether life expectancy follows an increasing or decreasing trend. Time-Series Decomposition: Separate data into trend, seasonality, and residuals.
2. Descriptive Statistics & Comparative Summary Objective: Compare summary statistics between years or groups. Tests/Methods: Mean, Median, Standard Deviation: Compare distributions of life expectancy, GDP, or schooling. Boxplots & Histograms: Show variations over different years or between developing vs. developed countries. Coefficient of Variation (CV): Compare variability in life expectancy across regions.
3. Correlation & Regression Analysis Objective: Examine relationships between variables. Methods: Pearson/Spearman Correlation: Check relationships between life expectancy and GDP, health expenditure, etc. Multiple Linear Regression: Predict life expectancy based on GDP, immunization, and schooling. Multicollinearity (VIF Test): Ensure independent variables are not highly correlated.
4. Hypothesis Testing (Comparative Analysis) Test Objective When to Use? t-Test (Independent Samples) Compare life expectancy between developed & developing nations Two groups (e.g., 2000 vs. 2015, or developed vs. developing) Paired t-Test Compare life expectancy in the same country over two time periods Before/after comparison (e.g., 2000 vs. 2015 for the same country) ANOVA (One-Way) Compare life expectancy across multiple groups More than two groups (e.g., continents or income groups) Chi-Square Test Test if categorical distributions (e.g., immunization coverage) differ over time Categorical variables (e.g., immunization rates vs. year)
5. Clustering & Classification (Machine Learning) Objective: Group countries based on life expectancy patterns. Methods: K-Means Clustering: Identify groups with similar life expectancy trends. Hierarchical Clustering: Create a country similarity tree. Decision Trees/Random Forest: Classify countries based on development status using life expectancy factors.
6. Forecasting Future Trends Objective: Predict life expectancy in future years using historical data. Methods: ARIMA (AutoRegressive Integrated Moving Average): Time-series forecasting. Exponential Smoothing: Forecast gradual trends. Machine Learning (LSTM, XGBoost): Predict based on multiple indicators.
7. Comparative Regional Analysis O...

From the mid-19th century until today, life expectancy at birth in the United States has roughly doubled, from 39.4 years in 1850 to 79.6 years in 2025. It is estimated that life expectancy in the U.S. began its upward trajectory in the 1880s, largely driven by the decline in infant and child mortality through factors such as vaccination programs, antibiotics, and other healthcare advancements. Improved food security and access to clean water, as well as general increases in living standards (such as better housing, education, and increased safety) also contributed to a rise in life expectancy across all age brackets. There were notable dips in life expectancy; with an eight year drop during the American Civil War in the 1860s, a seven year drop during the Spanish Flu empidemic in 1918, and a 2.5 year drop during the Covid-19 pandemic. There were also notable plateaus (and minor decreases) not due to major historical events, such as that of the 2010s, which has been attributed to a combination of factors such as unhealthy lifestyles, poor access to healthcare, poverty, and increased suicide rates, among others. However, despite the rate of progress slowing since the 1950s, most decades do see a general increase in the long term, and current UN projections predict that life expectancy at birth in the U.S. will increase by another nine years before the end of the century.

The research on life expectancy in countries takes the spotlight in the notebook's machine learning model. Substantial data analysis and predictive algorithms are used to uncover the reasons causing differences in longevity among countries. With the aid of strong statistical tools, valuable insights into the complex link between healthcare, socioeconomic factors, and life expectancy are sought |Description|Column| |:------:|:--------:| |Country under study|Country| |year|Year| |Status of the country's development|Status| |Population of country|Population| |Percentage of people finally one year old who were immunized against hepatitis B|Hepatitis B| |The number of reported measles cases per 1000 people|Measles| |Percentage of 1-year-olds immunized against polio|Polio| |Percentage of people finally one year old who were immunized against diphtheria|Diphtheria| |The number of deaths caused by AIDS of the last 4-year-olds who were born alive per 1000 people|HIV/AIDS| |The number of infant deaths per 1000 people|infant deaths| |he number of deaths of people under 5 years old per 1000 people|under-five deaths| |The ratio of government medical-health expenses to total government expenses in percentage|Total expenditure| |Gross domestic product|GDP| |The average body mass index of the entire population of the country|BMI| |Prevalence of thinness among people 19 years old in percentage|thinness 1-19 years| |Liters of alcohol consumption among people over 15 years old|Alcohol| |The number of years that people study|Schooling| |Country life expectancy|Life expectancy [target variable]|

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.

Effect of suicide rates on life expectancy dataset

Abstract
In 2015, approximately 55 million people died worldwide, of which 8 million committed suicide. In the USA, one of the main causes of death is the aforementioned suicide, therefore, this experiment is dealing with the question of how much suicide rates affects the statistics of average life expectancy.
The experiment takes two datasets, one with the number of suicides and life expectancy in the second one and combine data into one dataset. Subsequently, I try to find any patterns and correlations among the variables and perform statistical test using simple regression to confirm my assumptions.

Data

The experiment uses two datasets - WHO Suicide Statistics[1] and WHO Life Expectancy[2], which were firstly appropriately preprocessed. The final merged dataset to the experiment has 13 variables, where country and year are used as index: Country, Year, Suicides number, Life expectancy, Adult Mortality, which is probability of dying between 15 and 60 years per 1000 population, Infant deaths, which is number of Infant Deaths per 1000 population, Alcohol, which is alcohol, recorded per capita (15+) consumption, Under-five deaths, which is number of under-five deaths per 1000 population, HIV/AIDS, which is deaths per 1 000 live births HIV/AIDS, GDP, which is Gross Domestic Product per capita, Population, Income composition of resources, which is Human Development Index in terms of income composition of resources, and Schooling, which is number of years of schooling.

LICENSE

THE EXPERIMENT USES TWO DATASET - WHO SUICIDE STATISTICS AND WHO LIFE EXPECTANCY, WHICH WERE COLLEECTED FROM WHO AND UNITED NATIONS WEBSITE. THEREFORE, ALL DATASETS ARE UNDER THE LICENSE ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 3.0 IGO (https://creativecommons.org/licenses/by-nc-sa/3.0/igo/).

[1] https://www.kaggle.com/szamil/who-suicide-statistics

[2] https://www.kaggle.com/kumarajarshi/life-expectancy-who

Life expectancy in India was 25.4 in the year 1800, and over the course of the next 220 years, it has increased to almost 70. Between 1800 and 1920, life expectancy in India remained in the mid to low twenties, with the largest declines coming in the 1870s and 1910s; this was because of the Great Famine of 1876-1878, and the Spanish Flu Pandemic of 1918-1919, both of which were responsible for the deaths of up to six and seventeen million Indians respectively; as well as the presence of other endemic diseases in the region, such as smallpox. From 1920 onwards, India's life expectancy has consistently increased, but it is still below the global average.

This scatter chart displays death rate (per 1,000 people) against life expectancy at birth (year). The data is about continents.

Life expectancy in the United Kingdom was below 39 years in the year 1765, and over the course of the next two and a half centuries, it is expected to have increased by more than double, to 81.1 by the year 2020. Although life expectancy has generally increased throughout the UK's history, there were several times where the rate deviated from its previous trajectory. These changes were the result of smallpox epidemics in the late eighteenth and early nineteenth centuries, new sanitary and medical advancements throughout time (such as compulsory vaccination), and the First world War and Spanish Flu epidemic in the 1910s.

It is only in the past two centuries where demographics and the development of human populations has emerged as a subject in its own right, as industrialization and improvements in medicine gave way to exponential growth of the world's population. There are very few known demographic studies conducted before the 1800s, which means that modern scholars have had to use a variety of documents from centuries gone by, along with archeological and anthropological studies, to try and gain a better understanding of the world's demographic development. Genealogical records One such method is the study of genealogical records from the past; luckily, there are many genealogies relating to European families that date back as far as medieval times. Unfortunately, however, all of these studies relate to families in the upper and elite classes; this is not entirely representative of the overall population as these families had a much higher standard of living and were less susceptible to famine or malnutrition than the average person (although elites were more likely to die during times of war). Nonetheless, there is much to be learned from this data. Impact of the Black Death In the centuries between 1200 and 1745, English male aristocrats who made it to their 21st birthday were generally expected to live to an age between 62 and 72 years old. The only century where life expectancy among this group was much lower was in the 1300s, where the Black Death caused life expectancy among adult English noblemen to drop to just 45 years. Experts assume that the pre-plague population of England was somewhere between four and seven million people in the thirteenth century, and just two million in the fourteenth century, meaning that Britain lost at least half of its population due to the plague. Although the plague only peaked in England for approximately eighteen months, between 1348 and 1350, it devastated the entire population, and further outbreaks in the following decades caused life expectancy in the decade to drop further. The bubonic plague did return to England sporadically until the mid-seventeenth century, although life expectancy among English male aristocrats rose again in the centuries following the worst outbreak, and even peaked at more than 71 years in the first half of the sixteenth century.

IntroductionAlthough child and adolescent health is the core of the global health agenda, the cause of death and its expected contribution to life expectancy (LE) among those aged 5–14 are under-researched across countries, especially in low- and middle-income countries (LMICs).MethodsDeath rates per 10 years age group including a 5–14-year-old group were calculated by the formula, which used the population and the number of deaths segmented by the cause of death and gender from the 2019 Global Burden of Disease (GBD) study. LE and cause-eliminated LE in 10-year intervals were calculated by using life tables.ResultsIn 2019, the global mortality rate for children and adolescents aged 5–14 years was 0.522 (0.476–0.575) per 1,000, and its LF was 71.377 years. In different-income regions, considerable heterogeneity remains in the ranking of cause of death aged 5–14 years. The top three causes of death in low-income countries (LICs) are enteric infections [0.141 (0.098–0.201) per 1,000], other infectious diseases [0.103 (0.073–0.148) per 1,000], and neglected tropical diseases and malaria [0.102 (0.054–0.172) per 1,000]. Eliminating these mortality rates can increase the life expectancy of the 5–14 age group by 0.085, 0.062, and 0.061 years, respectively. The top three causes of death in upper-middle income countries (upper MICs) are unintentional injuries [0.066 (0.061–0.072) per 1,000], neoplasm [0.046 (0.041–0.050) per 1,000], and transport injuries [0.045 (0.041–0.049) per 1,000]. Eliminating these mortality rates can increase the life expectancy of the 5–14 age group by 0.045, 0.031, and 0.030 years, respectively.ConclusionThe mortality rate for children and adolescents aged 5–14 years among LMICs remains high. Considerable heterogeneity was observed in the main causes of death among regions. According to the main causes of death at 5–14 years old in different regions and countries at different economic levels, governments should put their priority in tailoring their own strategies to decrease preventable mortality.

This scatter chart displays birth rate (per 1,000 people) against life expectancy at birth (year). The data is about countries.

BackgroundThe human immunodeficiency virus (HIV) has caused a lot of havoc since the early 1970s, affecting 37.6 million people worldwide. The 90-90-90 treatment policy was adopted in Ghana in 2015 with the overall aim to end new infections by 2030, and to improve the life expectancy of HIV seropositive individuals. With the scale-up of Highly Active Antiretroviral Therapy, the lifespan of People Living with HIV (PLWH) on antiretrovirals (ARVs) is expected to improve. In rural districts in Ghana, little is known about the survival probabilities of PLWH on ARVs. Hence, this study was conducted to estimate the survival trends of PLWH on ARVs.MethodsA retrospective evaluation of data gathered across ARV centres within Tatale and Zabzugu districts in Ghana from 2016 to 2020 among PLWH on ARVs. A total of 261 participants were recruited for the study. The data was analyzed using STATA software version 16.0. Lifetable analysis and Kaplan-Meier graph were used to assess the survival probabilities. “Stptime” per 1000 person-years and the competing risk regression were used to evaluate mortality rates and risk.ResultsThe cumulative survival probability was 0.8847 (95% CI: 0.8334–0.9209). The overall mortality rate was 51.89 (95% CI: 36.89–72.97) per 1000 person-years. WHO stage III and IV [AHR: 4.25 (95%CI: 1.6–9.71) p = 0.001] as well as age group (50+ years) [AHR: 5.02 (95% CI: 1.78–14.13) p = 0.002] were associated with mortality.ConclusionSurvival probabilities were high among the population of PLWH in Tatale and Zabzugu with declining mortality rates. Clinicians should provide critical attention and care to patients at HIV WHO stages III and IV and intensify HIV screening at all entry points since early diagnosis is associated with high survival probabilities.

Life expectancy in Japan was 36.4 in the year 1860, and over the course of the next 160 years, it is expected to have increased to 84.4, which is the second highest in the world (after Monaco). Although life expectancy has generally increased throughout Japan's history, there were several times where the rate deviated from its previous trajectory. These changes were a result of the Spanish Flu in the 1910s, the Second World War in the 1940s, and the sharp increase was due to the high rate of industrialization and economic prosperity in Japan, in the mid-twentieth century.

This scatter chart displays life expectancy at birth (year) against death rate (per 1,000 people) in Cuba. The data is about countries per year.

This scatter chart displays death rate (per 1,000 people) against life expectancy at birth (year) in Liberia. The data is filtered where the date is 2021. The data is about countries per year.

The average for 2023 based on 196 countries was 73.65 years. The highest value was in Monaco: 86.37 years and the lowest value was in Nigeria: 54.46 years. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

Age-standardized rate of years of life lost in Ethiopia, from 1990 to 2015.

In the world's most populous country, life expectancy has been continuously rising over the last decades, benefitting greatly from China's economic ascendance. In 2022, average life expectancy at birth in China reached about 78.6 years. Life expectancy at birth Life expectancy at birth refers to the average number of years a group of people born in the same year would live, assuming constant mortality rates. San Marino and Monaco had the highest life expectancy at birth, while China had reached a life expectancy above global average. People who were born in San Marino or Monaco in 2023 had a life expectancy of approximately 87 years or 86 years on average respectively. Demographic development in China Whereas average life expectancy at birth has been growing steadily, birth rates in China have been experiencing a slowdown. In 2024, about 6.77 babies had been born per 1,000 women in China, the second lowest point in the recent decade. As a result of low fertility rates and the extended life expectancy in China, the share of elderly people had been rising rapidly. The number of Chinese population aged 60 and older had more than doubled over the past three decades and is projected to reach its peak at 504 million in 2050. People aged 60 and older have been estimated to account for approximately one fourth of China’s total population by 2030, indicating a sharp climb from just around 13 percent in 2010. In order to pinpoint this massive shift in the age pyramid of China, an important indicator for measuring the pressure of aging population on productive population may be consulted. The old-age dependency ratio in China was expected to reach 52.3 percent in 2050.