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

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 ...).

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!

Do women live longer than men? How long? Does it happen everywhere? Is life expectancy increasing? Everywhere? Which is the country with the lowest life expectancy? Which is the one with the highest? In this project, we will answer all these questions by manipulating and visualizing United Nations life expectancy data using ggplot2.

The dataset can be found here and contains the average life expectancies of men andwomen by country (in years). It covers four periods: 1985-1990, 1990-1995, 1995-2000, and 2000-2005.

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

This dataset presents the average number of years a woman aged 65 can expect to live in good health, known as healthy life expectancy (HLE). It is a key measure of quality of life in later years and reflects both longevity and the prevalence of good health among older women.

Rationale Increasing healthy life expectancy at age 65 for females is a major public health goal. It highlights the importance of not only living longer but also maintaining good health and independence in later life. This indicator supports the planning of health and social care services and helps assess the impact of health inequalities and lifestyle factors on aging populations.

Numerator The numerator is derived from the number of deaths registered in the respective calendar years and the weighted prevalence of individuals reporting good or very good health, as captured by the Annual Population Survey (APS). Data are provided by the Office for National Statistics (ONS).

Denominator The denominator is based on population estimates from the 2021 Census and the APS sample, weighted to reflect local authority population totals. These data are also provided by the ONS.

Caveats Healthy life expectancy figures exclude residents of communal establishments, except for NHS housing and students in halls of residence who are included based on their parents' address. This may affect comparability in areas with large institutional populations.

External References Fingertips Public Health Profiles – Healthy Life Expectancy (Female)

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

This table contains mortality indicators by sex for Canada and all provinces except Prince Edward Island. These indicators are derived from three-year complete life tables. Mortality indicators derived from single-year life tables are also available (table 13-10-0837). For Prince Edward Island, Yukon, the Northwest Territories and Nunavut, mortality indicators derived from three-year abridged life tables are available (table 13-10-0140).

LivWell is a global longitudinal database which provides a range of key indicators related to women’s socioeconomic status, health and well-being, access to basic services, and demographic outcomes. Data are available at the sub-national level for 52 countries and 447 regions. A total of 134 indicators are based on 199 Demographic and Health Surveys for the period 1990-2019, supplemented by extensive information on socioeconomic and climatic conditions in the respective regions for a total of 190 indicators. The resulting data offer various opportunities for policy-relevant research on gender inequality, inclusive development, and demographic trends at the sub-national level.

For a full description, please refer to the article describing the database here: https://www.nature.com/articles/s41597-022-01824-2

The companion repository livwelldata allows to easily use the database in R. The R package can be downloaded following the instructions on the following git repository: https://gitlab.pik-potsdam.de/belmin/livwelldata. The version of the database in the package is the same as in this repository.

This dataset is about countries per year in Costa Rica. It has 64 rows. It features 4 columns: country, life expectancy at birth, and proportion of seats held by women in national parliaments.

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).

Dataset replaced by: http://data.europa.eu/euodp/data/dataset/tHJ7RfJO3ZAXvnwP5Jm5kw The indicator Healthy Life Years (HLY) at age 65 measures the number of years that a person at age 65 is still expected to live in a healthy condition. HLY is a health expectancy indicator which combines information on mortality and morbidity. The data required are the age-specific prevalence (proportions) of the population in healthy and unhealthy conditions and age-specific mortality information. A healthy condition is defined by the absence of limitations in functioning/disability. The indicator is calculated separately for males and females. The indicator is also called disability-free life expectancy (DFLE). Life expectancy at age 65 is defined as the mean number of years still to be lived by a person at age 65, if subjected throughout the rest of his or her life to the current mortality conditions.

About this dataset

Context

The dataset tabulates the population of Live Oak by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Live Oak. The dataset can be utilized to understand the population distribution of Live Oak by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Live Oak. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Live Oak.

Key observations

Largest age group (population): Male # 0-4 years (465) | Female # 15-19 years (435). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Live Oak population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Live Oak is shown in the following column.
• Population (Female): The female population in the Live Oak is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Live Oak for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Live Oak Population by Gender. You can refer the same here

The Study of Women's Health Across the Nation (SWAN), is a multi-site longitudinal, epidemiologic study designed to examine the health of women during their middle years. The study examines the physical, biological, psychological, and social changes during this transitional period. The goal of SWAN's research is to help scientists, health care providers, and women learn how mid-life experiences affect health and quality of life during aging. The data include questions about doctor visits, medical conditions, medications, treatments, medical procedures, relationships, smoking, and menopause related information such as age at pre-, peri- and post-menopause, self-attitudes, feelings, and common physical problems associated with menopause.The study is co-sponsored by the National Institute on Aging (NIA), the National Institute of Nursing Research (NINR), the National Institutes of Health (NIH), and the NIH Office of Research on Women's Health. The study began in 1994. Between 1996 and 1997, 3,302 participants joined SWAN through 7 designated research centers. The research centers are located in the following communities: Detroit, MI; Boston, MA; Chicago, IL; Oakland and Los Angeles, CA; Newark, NJ; and Pittsburgh, PA. SWAN participants represent five racial/ethnic groups and a variety of backgrounds and cultures. This is the next phase of data collection after the original collection of the screening data (ICPSR 4368).

This dataset is about countries per year in Spain. It has 64 rows. It features 4 columns: country, life expectancy at birth, and proportion of seats held by women in national parliaments.

This dataset is about countries per year in New Zealand. It has 1 row and is filtered where the date is 2021. It features 4 columns: country, life expectancy at birth, and proportion of seats held by women in national parliaments.

This dataset is about countries in Western Africa. It has 16 rows. It features 3 columns: life expectancy at birth, and proportion of seats held by women in national parliaments.

Public Health Indicators in Chicago

Natality, Mortality, Infectious Disease, Lead Poisoning and Economic Status

By City of Chicago [source]

About this dataset

This public health dataset contains a comprehensive selection of indicators related to natality, mortality, infectious disease, lead poisoning, and economic status from Chicago community areas. It is an invaluable resource for those interested in understanding the current state of public health within each area in order to identify any deficiencies or areas of improvement needed.

The data includes 27 indicators such as birth and death rates, prenatal care beginning in first trimester percentages, preterm birth rates, breast cancer incidences per hundred thousand female population, all-sites cancer rates per hundred thousand population and more. For each indicator provided it details the geographical region so that analyses can be made regarding trends on a local level. Furthermore this dataset allows various stakeholders to measure performance along these indicators or even compare different community areas side-by-side.

This dataset provides a valuable tool for those striving toward better public health outcomes for the citizens of Chicago's communities by allowing greater insight into trends specific to geographic regions that could potentially lead to further research and implementation practices based on empirical evidence gathered from this comprehensive yet digestible selection of indicators

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How to use the dataset

In order to use this dataset effectively to assess the public health of a given area or areas in the city: - Understand which data is available: The list of data included in this dataset can be found above. It is important to know all that are included as well as their definitions so that accurate conclusions can be made when utilizing the data for research or analysis. - Identify areas of interest: Once you are familiar with what type of data is present it can help to identify which community areas you would like to study more closely or compare with one another. - Choose your variables: Once you have identified your areas it will be helpful to decide which variables are most relevant for your studies and research specific questions regarding these variables based on what you are trying to learn from this data set.
- Analyze the Data : Once your variables have been selected and clarified take right into analyzing the corresponding values across different community areas using statistical tests such as t-tests or correlations etc.. This will help answer questions like “Are there significant differences between two outputs?” allowing you to compare how different Chicago Community Areas stack up against each other with regards to public health statistics tracked by this dataset!

Research Ideas

• Creating interactive maps that show data on public health indicators by Chicago community area to allow users to explore the data more easily.
• Designing a machine learning model to predict future variations in public health indicators by Chicago community area such as birth rate, preterm births, and childhood lead poisoning levels.
• Developing an app that enables users to search for public health information in their own community areas and compare with other areas within the city or across different cities in the US

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: public-health-statistics-selected-public-health-indicators-by-chicago-community-area-1.csv | Column name | Description | |:-----------------------------------------------|:--------------------------------------------------------------------------------------------------| | Community Area | Unique identifier for each community area in Chicago. (Integer) | | Community Area Name | Name of the community area in Chicago. (String) | | Birth Rate | Number of live births per 1,000 population. (Float) | | General Fertility Rate | Number of live births per 1,000 women aged 15-44. (Float) ...

The dataset contains information on various demographic and health indicators for different countries. It is organized into several columns, each providing essential information about these countries. Here's a description of each column:

1. Country: This column represents the names of different countries or regions included in the dataset. Each row corresponds to a specific country or region, and this column serves as the identifier for each entry.

2. Life Expectancy Males: This column contains data on the average life expectancy of males in each of the listed countries. Life expectancy is a crucial health indicator and provides an estimate of the average number of years a male can expect to live, given current mortality rates and health conditions.

3. Life Expectancy Females: Similar to the "Life Expectancy Males" column, this column provides data on the average life expectancy of females in the same countries. It reflects the average number of years a female can expect to live, considering the prevailing health and mortality conditions.

4. Birth Rate: The "Birth Rate" column contains information about the birth rate in each country. Birth rate is a demographic indicator that represents the number of live births per 1,000 people in a given population over a specific period, usually a year. It can provide insights into a country's population growth or decline.

5. Death Rate: This column presents data on the death rate in each of the listed countries. The death rate is another crucial demographic indicator and represents the number of deaths per 1,000 people in a population over a specific period, often a year. It helps gauge the overall health and mortality conditions within a country.

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

Live Long: What Really Extends Lifespan?

What factors will really increase your average life expectancy and lifespan?

What will really increase your average life expectancy and lifespan?

Why do women live longer than men?

What’s the best method of life extension?

Diet and exercise?

Or polygamy and pets?

Let the latest data decide.