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...

1) Data Introduction • The Life Expectancy (WHO) Dataset is a WHO-based national health dataset that tabulates life expectancy, vaccination, mortality, economy, and society in 193 countries around the world from 2000 to 2015.

2) Data Utilization (1) Life Expectancy (WHO) Dataset has characteristics that: • Each row contains more than 20 health, economic, and social variables and target variables (life expectancy), including country, year, life expectancy, vaccination rates (e.g., hepatitis B, polio, diphtheria), infant and adult mortality, GDP, population, education level, drinking and smoking. • Although some missing values exist in the data, they are well structured for analysis of health levels and influencing factors by country, including data from various countries and time series. (2) Life Expectancy (WHO) Dataset can be used to: • Analysis of factors affecting life expectancy: The effects of various factors such as vaccination, mortality, economic and social variables on life expectancy can be assessed using statistical methods such as regression analysis. • Health Policy and International Comparative Study: Using national and annual health indicators, it can be used for international health research, such as evaluating the effectiveness of health policies, analyzing health gaps, and establishing strategies to support low-income countries.

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?

This dataset shows the life expectancy at regional level for 2011.

Life expectancy in the EU, which is a reflection of well-being, is among the highest in the world. Of the 50 countries in the world with the highest life expectancy in 2012, 21 were EU Member States, 18 of which had a higher life expectancy than the US. Differences between regions in the EU are marked. Life expectancy at birth is less than 74 in many partsof Bulgaria as well as in Latvia and Lithuania, while overall across the EU it is over 80 years in two out of every three regions. In 17 regions in Spain, France and Italy, it is 83 years or more.

EU-28 = 80.3 . BE, IT, UK: 2010. Source: Eurostat

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 data set has been generated using data from the Gapminder website, which focuses on gathering and sharing statistics and other information about social, economic and environmental development at local, national and global levels.

This particular data set describes the values of several parameters (see the list below) between 1998 and 2018 for a total of 175 countries, having a total of 3675 rows. The parameters included in the data set and the column name of the dataframe are as follows:

• Country (country). Describes the country name
• Continent (continent). Describes the continent to which the country belongs
• Year (year). Describes the year to which the data belongs
• Life expectancy (life_exp). Describes the life expectancy for a given country in a given year
• Human Development Index (hdi_index). Describes the HDI index value for a given country in a given year
• CO2 emissions per person(co2_consump). Describes the CO2 emissions in tonnes per person for a given country in a given year
• Gross Domestic Product per capita (gdp). Describes the GDP per capita in dollars for a given country in a given year
• % Service workers (services). Describes the the % of service workers for a given country in a given year

Spain ES: Life Expectancy at Birth: Total data was reported at 82.832 Year in 2016. This stayed constant from the previous number of 82.832 Year for 2015. Spain ES: Life Expectancy at Birth: Total data is updated yearly, averaging 76.747 Year from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 83.229 Year in 2014 and a record low of 69.109 Year in 1960. Spain ES: Life Expectancy at Birth: Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Spain – Table ES.World Bank: Health Statistics. 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.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision, or derived from male and female life expectancy at birth from sources such as: (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

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

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

Country: Name of the country.

Density (P/Km2): Population density measured in persons per square kilometer.

Abbreviation: Abbreviation or code representing the country.

Agricultural Land (%): Percentage of land area used for agricultural purposes.

Land Area (Km2): Total land area of the country in square kilometers.

Armed Forces Size: Size of the armed forces in the country.

Birth Rate: Number of births per 1,000 population per year.

Calling Code: International calling code for the country.

Capital/Major City: Name of the capital or major city.

CO2 Emissions: Carbon dioxide emissions in tons.

CPI: Consumer Price Index, a measure of inflation and purchasing power.

CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.

Currency_Code: Currency code used in the country.

Fertility Rate: Average number of children born to a woman during her lifetime.

Forested Area (%): Percentage of land area covered by forests.

Gasoline_Price: Price of gasoline per liter in local currency.

GDP: Gross Domestic Product, the total value of goods and services produced in the country.

Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.

Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.

Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.

Largest City: Name of the country's largest city.

Life Expectancy: Average number of years a newborn is expected to live.

Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.

Minimum Wage: Minimum wage level in local currency.

Official Language: Official language(s) spoken in the country.

Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.

Physicians per Thousand: Number of physicians per thousand people.

Population: Total population of the country.

Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.

Tax Revenue (%): Tax revenue as a percentage of GDP.

Total Tax Rate: Overall tax burden as a percentage of commercial profits.

Unemployment Rate: Percentage of the labor force that is unemployed.

Urban Population: Percentage of the population living in urban areas.

Latitude: Latitude coordinate of the country's location.

Longitude: Longitude coordinate of the country's location.

Potential Use Cases

Analyze population density and land area to study spatial distribution patterns.

Investigate the relationship between agricultural land and food security.

Examine carbon dioxide emissions and their impact on climate change.

Explore correlations between economic indicators such as GDP and various socio-economic factors.

Investigate educational enrollment rates and their implications for human capital development.

Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.

Study labor market dynamics through indicators such as labor force participation and unemployment rates.

Investigate the role of taxation and its impact on economic development.

Explore urbanization trends and their social and environmental consequences.

Vietnam Total Life Expectancy at Birth: Whole Country data was reported at 73.400 Year in 2016. This records an increase from the previous number of 73.300 Year for 2015. Vietnam Total Life Expectancy at Birth: Whole Country data is updated yearly, averaging 73.000 Year from Dec 2005 (Median) to 2016, with 9 observations. The data reached an all-time high of 73.400 Year in 2016 and a record low of 72.200 Year in 2005. Vietnam Total Life Expectancy at Birth: Whole Country data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.G058: Vital Statistics.

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

This dataset contains replication files for "The Association Between Income and Life Expectancy in the United States, 2001-2014" by Augustin Bergeron, Raj Chetty, David Cutler, Benjamin Scuderi, Michael Stepner, and Nicholas Turner. For more information, see https://opportunityinsights.org/paper/lifeexpectancy/. A summary of the related publication follows. How can we reduce socioeconomic disparities in health outcomes? Although it is well known that there are significant differences in health and longevity between income groups, debate remains about the magnitudes and determinants of these differences. We use new data from 1.4 billion anonymous earnings and mortality records to construct more precise estimates of the relationship between income and life expectancy at the national level than was feasible in prior work. We then construct new local area (county and metro area) estimates of life expectancy by income group and identify factors that are associated with higher levels of life expectancy for low-income individuals. Our findings show that disparities in life expectancy are not inevitable. There are cities throughout America — from New York to San Francisco to Birmingham, AL — where gaps in life expectancy are relatively small or are narrowing over time. Replicating these successes more broadly will require targeted local efforts, focusing on improving health behaviors among the poor in cities such as Las Vegas and Detroit. Our findings also imply that federal programs such as Social Security and Medicare are less redistributive than they might appear because low-income individuals obtain these benefits for significantly fewer years than high-income individuals, especially in cities like Detroit. Going forward, the challenge is to understand the mechanisms that lead to better health and longevity for low-income individuals in some parts of the U.S. To facilitate future research and monitor local progress, we have posted annual statistics on life expectancy by income group and geographic area (state, CZ, and county) at The Health Inequality Project website. Using these data, researchers will be able to study why certain places have high or improving levels of life expectancy and ultimately apply these lessons to reduce health disparities in other parts of the country.

BackgroundThe rising prevalence of cognitive impairment is an increasing challenge with the ageing of our populations but little is known about the burden in low- and middle- income Latin American and Caribbean countries (LAC) that are aging more rapidly than their developed counterparts. We examined life expectancies with cognitive impairment (CILE) and free of cognitive impairment (CIFLE) in seven developing LAC countries.MethodsData from The Survey on Health, Well-being and Ageing in LAC (N = 10,597) was utilised and cognitive status was assessed by the Mini-Mental State Examination (MMSE). The Sullivan Method was applied to estimate CILE and CIFLE. Logistic regression was used to determine the effect of age, gender and education on cognitive outcome. Meta-regression models were fitted for all 7 countries together to investigate the relationship between CIFLE and education in men and women at age 60.ResultsThe prevalence of CI increased with age in all countries except Uruguay and with a significant gender effect observed only in Mexico where men had lower odds of CI compared to women [OR = 0.464 95% CInt (0.268 – 0.806)]. Low education was associated with increased prevalence of CI in Brazil [OR = 4.848 (1.173–20.044)], Chile [OR = 3.107 (1.098–8.793), Cuba [OR = 2.295 (1.247–4.225)] and Mexico [OR = 3.838 (1.368–10.765). For males, total life expectancy (TLE) at age 60 was highest in Cuba (19.7 years) and lowest in Brazil and Uruguay (17.6 years). TLE for females at age 60 was highest for Chileans (22.8 years) and lowest for Brazilians (20.2 years). CIFLE for men was greatest in Cuba (19.0 years) and least in Brazil (16.7 years). These differences did not appear to be explained by educational level (Men: p = 0.408, women: p = 0.695).ConclusionIncreasing age, female sex and low education were associated with higher CI in LAC reflecting patterns found in other countries.

Romania RO: Life Expectancy at Birth: Male data was reported at 71.500 Year in 2016. This stayed constant from the previous number of 71.500 Year for 2015. Romania RO: Life Expectancy at Birth: Male data is updated yearly, averaging 66.700 Year from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 71.600 Year in 2013 and a record low of 63.860 Year in 1960. Romania RO: Life Expectancy at Birth: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Romania – Table RO.World Bank: Health Statistics. 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.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

National Records of Scotland Guidance;What is ‘period’ life expectancyAll of the estimates presented in this report are ‘period’ life expectancy. They are calculated assuming that mortality rates for each age group in the time period (here 2021-2023) are constant throughout a person’s life. Period life expectancy is often described as how long a baby born now could expect to live if they experienced today’s mortality rates throughout their lifetime. It is very unlikely that this would be the case as it means that future changes in things such as medicine and legislation are not taken into consideration.Period life expectancy is not an accurate prediction of how long a person born today will actually live, but it is a useful measure of population health at a point in time and is most useful for comparing trends over time, between areas of a country and with other countries.How national life expectancy is calculatedThe latest life expectancy figures are calculated from the mid-year population estimates for Scotland and the number of deaths registered in Scotland during 2021, 2022, and 2023. Life expectancy for Scotland is calculated for each year of age and represents the average number of years that someone of that age could expect to live if death rates for each age group remained constant over their lifetime. Life expectancy in Scotland is calculated as a three-year average, produced by combining deaths and population data for the three-year period. Three years of data are needed to provide large enough numbers to make these figures accurate and lessen the effect of very ‘good’ or ‘bad’ years. Throughout this publication, the latest life expectancy figures refer to 2021-2023 period. How sub-national life expectancy is calculatedWe calculate life expectancy for areas within Scotland using a very similar method to the national figures but with a few key differences. Firstly, we use age groups rather than single year of age. This is to increase the population size of each age group to reduce fluctuations and ensure accurate calculation of mortality rates. Secondly, we use a maximum age group of 90+ whereas the national figures are calculated up to age 100. These are known as ‘abridged life tables.’ Because these methods produce slightly different figures, we also calculate a Scotland figure using the abridged method to allow for accurate comparisons between local areas for example. This Scotland figure is only for comparison and does not replace the headline national figure. You can read more information about the methods in this publication in our methodology guide on the NRS website. Uses of life expectancyLife expectancy at birth is a very useful indicator of mortality conditions across a population at a particular point in time. It also provides an objective means of comparing trends in mortality over time, between areas of a country and with other countries. This is used to monitor and investigate health inequalities and to set public health targets. Life expectancy is also used to inform pensions policy, research and teaching.

Norway NO: Life Expectancy at Birth: Male data was reported at 80.900 Year in 2016. This records an increase from the previous number of 80.500 Year for 2015. Norway NO: Life Expectancy at Birth: Male data is updated yearly, averaging 73.040 Year from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 80.900 Year in 2016 and a record low of 70.780 Year in 1963. Norway NO: Life Expectancy at Birth: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Norway – Table NO.World Bank: Health Statistics. 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.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

Exposure to ambient fine particulate matter (PM2.5) air pollution is a major risk for premature death. Here, we systematically quantify the global impact of PM2.5 on life expectancy. Using data from the Global Burden of Disease project and actuarial standard life table methods, we estimate global and national decrements in life expectancy that can be attributed to ambient PM2.5 for 185 countries. In 2016, PM2.5 exposure reduced average global life expectancy at birth by ∼1 year with reductions of ∼1.2–1.9 years in polluted countries of Asia and Africa. If PM2.5 in all countries met the World Health Organization Air Quality Guideline (10 μg m–3), we estimate life expectancy could increase by a population-weighted median of 0.6 year (interquartile range of 0.2–1.0 year), a benefit of a magnitude similar to that of eradicating lung and breast cancer. Because background disease rates modulate the effect of air pollution on life expectancy, high age-specific rates of cardiovascular disease in many polluted low- and middle-income countries amplify the impact of PM2.5 on survival. Our analysis adds to prior research by illustrating how mortality from air pollution substantially reduces human longevity.

The first edition of the World Happiness Report was published on April 1, 2012 (omitted from this dataset), with a methodology that ranks countries based on their Happiness Index. Since its release, this report has garnered global recognition and has been issued on a yearly basis, excluding 2014.

Happiness Index is explained by: • Dystopia (adds 1.83 Index score) + residual • GDP per capita • Social support • Healthy life expectancy • Freedom to make life choices • Generosity • Perceptions of corruption.

Dataset provided in .xlsx and .csv formats. For convenience, dataset is provided with and without NULLs.

Possible use cases & questions: Initially the dataset was meant to be used for visualization practice with BI tools, for example, Tableau.

Clean dataset without nulls: 1) “Yearly average Happiness Index change” 2) “Are there countries who’s happiness was increasing for years, but had a sudden drop in recent years?” 3) “Is there a year(s) where the average world happiness decreased compared to last year?” 4) Geo data visualization.

With nulls: 1) Practice dealing with nulls. 2) “Which countries in which year had no ranking?” Deep dive and exploration into possible causes (for example, war, internal conflict, government or policy changes, diseases) via other sources.

Columns: Country – country name. Year – year of the report. Index – Happiness Index score. Rank – country rank according to their Happiness Index score.

Reports used in this dataset: 1. The 2013 World Happiness Report provides rankings of 156 countries based on their happiness index during the period of 2010-2012. Data collected from: https://resources.unsdsn.org/world-happiness-report-2013 2. No report for 2014. 3. The 2015 World Happiness Report provides rankings of 158 countries based on their happiness index during the period of 2012-2014. 4. The 2016 World Happiness Report provides rankings of 157 countries based on their happiness index during the period of 2013-2015. 5. The 2017 World Happiness Report provides rankings of 155 countries based on their happiness index during the period of 2014-2016. 6. The 2018 World Happiness Report provides rankings of 156 countries based on their happiness index during the period of 2015-2017. 7. The 2019 World Happiness Report provides rankings of 156 countries based on their happiness index during the period of 2016-2018. 8. The 2020 World Happiness Report provides rankings of 153 countries based on their happiness index during the period of 2017-2019. 9. The 2021 World Happiness Report provides rankings of 149 countries based on their happiness index during the period of 2018-2020. 10. The 2022 World Happiness Report provides rankings of 146 countries based on their happiness index during the period of 2019-2021. 11. The 2023 World Happiness Report provides rankings of 137 countries based on their happiness index during the period of 2020-2022.

If not stated differently, data collected from: https://worldhappiness.report/

Licence: CDLA-Permissive-1.0

Notes: Please note that some country names have been shortened, for example, “Hong Kong Special Administrative Region of the People's Republic of China” was shortened to “Hong Kong”.

Additional notes: [name in older Reports, other data sources] = [name used in this file] [data source] • Czech Republic = Czechia [1] • Macedonia = North Macedonia [2] • Turkey = Turkiye [3]

Data sources: 1) https://european-union.europa.eu/principles-countries-history/country-profiles/czechia_en 2) https://www.strasbourg-europe.eu/macedonia/ 3) https://www.un.org/en/about-us/member-states/turkiye

Update History: 2023-03-14—2023-03-17 – initial data collection for 2013-2022. 2023-03-25 – updated for 2023.

The aim of the article is to compare health system outcomes in the BRICS countries, assess the trends of their changes in 2000−2017, and verify whether they are in any way correlated with the economic context. The indicators considered were: nominal and per capita current health expenditure, government health expenditure, gross domestic product (GDP) per capita, GDP growth, unemployment, inflation, and composition of GDP. The study covered five countries of the BRICS group over a period of 18 years. We decided to characterize countries covered with a dataset of selected indicators describing population health status, namely: life expectancy at birth, level of immunization, infant mortality rate, maternal mortality ratio, and tuberculosis case detection rate. We constructed a unified synthetic measure depicting the performance of individual health systems in terms of their outcomes with a single numerical value. Descriptive statistical analysis of quantitative traits consisted of the arithmetic mean (xsr), standard deviation (SD), and, where needed, the median. The normality of the distribution of variables was tested with the Shapiro–Wilk test. Spearman's rho and Kendall tau rank coefficients were used for correlation analysis between measures. The correlation analyses have been supplemented with factor analysis. We found that the best results in terms of health care system performance were recorded in Russia, China, and Brazil. India and South Africa are noticeably worse. However, the entire group performs visibly worse than the developed countries. The health system outcomes appeared to correlate on a statistically significant scale with health expenditures per capita, governments involvement in health expenditures, GDP per capita, and industry share in GDP; however, these correlations are relatively weak, with the highest strength in the case of government's involvement in health expenditures and GDP per capita. Due to weak correlation with economic background, other factors may play a role in determining health system outcomes in BRICS countries. More research should be recommended to find them and determine to what extent and how exactly they affect health system outcomes.

Laos LA: Life Expectancy at Birth: Male data was reported at 65.131 Year in 2016. This records an increase from the previous number of 64.806 Year for 2015. Laos LA: Life Expectancy at Birth: Male data is updated yearly, averaging 51.342 Year from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 65.131 Year in 2016 and a record low of 41.543 Year in 1960. Laos LA: Life Expectancy at Birth: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Laos – Table LA.World Bank: Health Statistics. 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.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;