In 2023, a woman in the United States aged 65 years could expect to live another **** years on average. This number decreased in the years 2020 and 2021, after reaching a high of **** years in 2019. Nevertheless, the life expectancy of a woman aged 65 years in the United States is still higher than that of a man of that age. In 2023, a man aged 65 years could be expected to live another 18.2 years on average. Why has the life expectancy in the U.S. declined? Overall, life expectancy in the United States has declined in recent years. In 2019, the life expectancy for U.S. women was **** years, but by 2023 it had decreased to **** years. Likewise, the life expectancy for men decreased from **** years to **** years in the same period. The biggest contributors to this decline in life expectancy are the COVID-19 pandemic and the opioid epidemic. Although deaths from the COVID-19 pandemic have decreased significantly since 2022, deaths from opioid overdose continue to increase, reaching all-time highs in 2022. The leading causes of death among U.S. women The leading causes of death among women in the United States in 2022 were heart disease, cancer, stroke, and COVID-19. That year, heart disease and cancer accounted for a combined **** percent of all deaths among women, while around *** percent of deaths were due to COVID-19. The overall leading causes of death in the United States generally reflect the leading causes among women, with some slight variations. For example, Alzheimer’s disease is the ***** leading cause of death among women but the ******* leading cause of death overall in the United States.

The life expectancy of women at birth in the United States stood at 81.1 years in 2023. Between 1960 and 2023, the life expectancy rose by eight years, though the increase followed an uneven trajectory rather than a consistent upward trend.

The average for 2022 based on 192 countries was 74.94 years. The highest value was in Macao: 87.98 years and the lowest value was in Nigeria: 53.97 years. The indicator is available from 1960 to 2022. Below is a chart for all countries where data are available.

In 2021, women had an average life expectancy of ** years at birth, while men were expected to live 68.9 years. The average life expectancy worldwide dropped from 2019 to 2021, primarily due to the COVID-19 pandemic. This statistic depicts the average life expectancy at birth worldwide in 1990, 2019, and 2021, by gender.

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

This dataset explores the intriguing phenomenon of life expectancy disparity between genders across various countries spanning the years 1950 to 2020. Delving into the age-old statement that "women live longer than men," this dataset provides insights into the evolving trends in life expectancy and population dynamics worldwide.

Dataset Glossary (Column-wise):

• Year: The year of observation (1950-2020).
• Female Life Expectancy: The average life expectancy at birth for females in a given year and country.
• Male Life Expectancy: The average life expectancy at birth for males in a given year and country.
• Population: The total population of the country in a given year.
• Life Expectancy Gap: The difference between female and male life expectancy, highlighting the disparity between genders.

The dataset aims to facilitate comprehensive analyses regarding gender-based life expectancy disparities over time and across different nations. Researchers, policymakers, and analysts can utilize this dataset to explore patterns, identify contributing factors, and devise strategies to address gender-based health inequalities.

License - This Dataset falls under the Creative Commons Attribution 3.0 IGO License. You can check the Terms of Use of this Data. If you want to learn more, visit the Website.

Acknowledgement: Image :- Freepik

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

PLEASE if you use or like this dataset UPVOTE 👁️

This dataset offers a detailed historical record of global life expectancy, covering data from 1960 to the present. It is meticulously curated to enable deep analysis of trends and gender disparities in life expectancy worldwide.

Dataset Structure & Key Columns:

Country Code (🔤): Unique identifier for each country.

Country Name (🌍): Official name of the country.

Region (🌐): Broad geographical area (e.g., Asia, Europe, Africa).

Sub-Region (🗺️): More specific regional classification within the broader region.

Intermediate Region (🔍): Additional granular geographical grouping when applicable.

Year (📅): The specific year to which the data pertains.

Life Expectancy for Women (👩‍⚕️): Average years a woman is expected to live in that country and year.

Life Expectancy for Men (👨‍⚕️): Average years a man is expected to live in that country and year.

Context & Use Cases:

This dataset is a rich resource for exploring long-term trends in global health and demography. By comparing life expectancy data over decades, researchers can:

Analyze Time Series Trends: Forecast future changes in life expectancy and evaluate the impact of health interventions over time.

Study Gender Disparities: Investigate the differences between life expectancy for women and men, providing insights into social, economic, and healthcare factors influencing these trends.

Regional & Sub-Regional Analysis: Compare and contrast life expectancy across various regions and sub-regions to understand geographical disparities and their underlying causes.

Support Public Policy Research: Inform policymakers by linking life expectancy trends with public health policies, socioeconomic developments, and other key indicators.

Educational & Data Science Applications: Serve as a comprehensive teaching tool for courses on public health, global development, and data analysis, as well as for Kaggle competitions and projects.

With its detailed, structured format and broad temporal coverage, this dataset is ideal for anyone looking to gain a nuanced understanding of global health trends and to drive impactful analyses in public health, social sciences, and beyond.

Feel free to ask for further customizations or additional details as needed!

In 2022 life expectancy for both males and females at birth fell when compared to 2021. Male life expectancy fell from 78.71 years to 78.57 years, and from 82.68 years to 82.57 years for women. Throughout most of this period, there is a steady rise in life expectancy for both males and females, with improvements in life expectancy beginning to slow in the 2010s and then starting to decline in the 2020s. Life expectancy since the 18th Century Although there has been a recent dip in life expectancy in the UK, long-term improvements to life expectancy stretch back several centuries. In 1765, life expectancy was below 39 years, and only surpassed 40 years in the 1810s, 50 years by the 1910s, 60 years by the 1930s and 70 by the 1960s. While life expectancy has broadly improved since the 1700s, this trajectory was interrupted at various points due to wars and diseases. In the early 1920s, for example, life expectancy suffered a noticeable setback in the aftermath of the First World War and Spanish Flu Epidemic. Impact of COVID-19 While improvements to UK life expectancy stalled during the 2010s, it wasn't until the 2020s that it began to decline. The impact of COVID-19 was one of the primary factors in this respect, with 2020 seeing the most deaths in the UK since 1918. The first wave of the pandemic in Spring of that year was a particularly deadly time, with weekly death figures far higher than usual. A second wave that winter saw a peak of almost 5,700 excess deaths a week in late January 2021, with excess deaths remaining elevated for several years afterward.

This table contains 2754 series, with data for years 2005/2007 - 2012/2014 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (153 items: Canada; Newfoundland and Labrador; Eastern Regional Integrated Health Authority, Newfoundland and Labrador; Central Regional Integrated Health Authority, Newfoundland and Labrador; ...); Age group (2 items: At birth; At age 65); Sex (3 items: Both sexes; Males; Females); Characteristics (3 items: Life expectancy; Low 95% confidence interval, life expectancy; High 95% confidence interval, life expectancy).

Note: This dataset is historical only and there are not corresponding datasets for more recent time periods. For that more-recent information, please visit the Chicago Health Atlas at https://chicagohealthatlas.org.

This dataset gives the average life expectancy and corresponding confidence intervals for each Chicago community area for the years 1990, 2000 and 2010. See the full description at: https://data.cityofchicago.org/api/views/qjr3-bm53/files/AAu4x8SCRz_bnQb8SVUyAXdd913TMObSYj6V40cR6p8?download=true&filename=P:\EPI\OEPHI\MATERIALS\REFERENCES\Life Expectancy\Dataset description - LE by community area.pdf

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Between 2021 and 2023, London was the region of the United Kingdom that had the highest average life expectancy for females, at ***** years, while South East England had the highest life expectancy for males at ***** years. By comparison, Scotland had the lowest life expectancy, at ***** for males and ***** for females.

Mexico: Life expectancy, in years, female: The latest value from 2022 is 78.16 years, an increase from 74.86 years in 2021. In comparison, the world average is 74.94 years, based on data from 192 countries. Historically, the average for Mexico from 1960 to 2022 is 71.23 years. The minimum value, 56.97 years, was reached in 1960 while the maximum of 78.16 years was recorded in 2022.

International estimates of mean life expectancy at age 40, by country for men and women

Women have a higher life expectancy than men in Sweden. Moreover, it increased for both gender over the past 10 years. For people born between 2019 and 2023, the life expectancy for men and women was 81.21 and 84.69 years, respectively. Sweden has one of the highest life expectancy levels worldwide.

Across the world, people are living longer. In 1900, the average life expectancy of a newborn was 32 years. By 2021 this had more than doubled to 71 years. But where, when, how, and why has this dramatic change occurred? To understand it, we can look at data on life expectancy worldwide. The large reduction in child mortality has played an important role in increasing life expectancy. But life expectancy has increased at all ages. Infants, children, adults, and the elderly are all less likely to die than in the past, and death is being delayed. This remarkable shift results from advances in medicine, public health, and living standards. Along with it, many predictions of the ‘limit’ of life expectancy have been broken.

Data Dictionary

life_expectancy.csv

life_expectancy_different_ages.csv

life_expectancy_female_male.csv

citation(tidytuesday)

The average for 2022 based on 47 countries was 77.29 years. The highest value was in Macao: 87.98 years and the lowest value was in Afghanistan: 66.21 years. The indicator is available from 1960 to 2022. Below is a chart for all countries where data are available.

Japan stands out as one of the countries with the highest population longevity, from a global perspective 🌏, having the highest estimated life expectancy at birth of 84.26 years. The longevity of Japanese women is notable, ranking first worldwide with a life expectancy of 86.94 years, while Japanese men rank second with 81.49 years (World Health Organization, 2020). Japan's high life expectancy can be attributed to various factors. Technological progress, especially in the medical field, along with the country's accelerated economic development, in recent decades, have inevitably led to an increase in the average life expectancy of the population.

The dataset contains information about life expectancy and economic&social variables for Japan's prefectures as of 2020. - Life expectancy data source: Ministry of Health, Labour and Welfare, Japan - Independent variables data source: Japanese Government Statistics - Geospatial prefecture data: GitHub

Overview This project analyzes life expectancy across countries, utilizing data from 2000 to 2015. The study examines how key socioeconomic and health factors influence life expectancy. Factors such as GDP, adult mortality, schooling, HIV/AIDS prevalence, and BMI are included in the analysis, which uses multiple linear regression and mixed-effects modeling to determine which variables significantly affect life expectancy.

Data Description The dataset includes life expectancy information and its influencing factors from various countries over a 15-year period (2000-2015). The data was sourced from the WHO Life Expectancy Dataset available on Kaggle. It comprises both continuous and categorical variables, including: • Life Expectancy (Dependent Variable): Average number of years an individual is expected to live. Continuous Variables: o GDP per capita o Adult Mortality (per 1000 individuals aged 15-65) o Schooling (mean years of education) o Alcohol consumption per capita Categorical Variables: o HIV/AIDS prevalence o Country status (Developed vs. Developing) o BMI category (Underweight, Normal, Overweight, Obese)

Problem Statement Life expectancy is a crucial metric for assessing the overall health and well-being of populations. It varies significantly between countries due to economic, social, and health factors. This project seeks to identify the most important variables that predict life expectancy, offering insights for policymakers on improving public health and longevity in their populations. Hypotheses 1. Higher GDP leads to higher life expectancy. 2. Higher adult mortality results in lower life expectancy. 3. More years of schooling increase life expectancy. 4. Higher HIV/AIDS prevalence reduces life expectancy. 5. Living in a developed country increases life expectancy. 6. Higher BMI (underweight or obese) correlates with reduced life expectancy. 7. Higher alcohol consumption reduces life expectancy.

Methodology • Data Preprocessing: Missing values were handled by imputation, and skewed variables (like GDP) were log-transformed to improve model performance. • Exploratory Data Analysis: Visualizations (histograms, scatterplots, and box plots) were used to understand the relationships between independent variables and life expectancy. Modeling: o Multiple Linear Regression was used to examine how each continuous and categorical variable impacts life expectancy. o Mixed-effects modeling was applied to account for country-specific effects, capturing variability across different nations.

Key Results 1. GDP: Log-transformed GDP had a significant positive effect on life expectancy, with an adjusted R² of 0.29. Higher income is positively correlated with longer life expectancy. 2. Adult Mortality: Increased adult mortality significantly reduced life expectancy. For every unit increase in adult mortality, life expectancy decreased by 0.042 years. 3. Schooling: More years of schooling was strongly correlated with longer life expectancy, reflecting the importance of education in enhancing health outcomes. 4. HIV/AIDS: Countries with higher HIV/AIDS prevalence had lower life expectancy, with significant negative coefficients for all levels of prevalence. 5. Country Status: Developed countries had significantly higher life expectancy than developing countries, with an average difference of about 1.52 years. 6. BMI: While underweight and obese categories were significant predictors, the relationship between BMI and life expectancy was complex, suggesting that high-income countries might offset health risks through medical care. 7. Alcohol Consumption: Contrary to initial expectations, alcohol consumption did not have a statistically significant effect on life expectancy in this model.