In 2023, Singapore dominated the ranking of the world's health and health systems, followed by Japan and South Korea. The health index score is calculated by evaluating various indicators that assess the health of the population, and access to the services required to sustain good health, including health outcomes, health systems, sickness and risk factors, and mortality rates. The health and health system index score of the top ten countries with the best healthcare system in the world ranged between 82 and 86.9, measured on a scale of zero to 100.

Global Health Security Index  Numerous health and health system indexes have been developed to assess various attributes and aspects of a nation's healthcare system. One such measure is the Global Health Security (GHS) index. This index evaluates the ability of 195 nations to identify, assess, and mitigate biological hazards in addition to political and socioeconomic concerns, the quality of their healthcare systems, and their compliance with international finance and standards. In 2021, the United States was ranked at the top of the GHS index, but due to multiple reasons, the U.S. government failed to effectively manage the COVID-19 pandemic. The GHS Index evaluates capability and identifies preparation gaps; nevertheless, it cannot predict a nation's resource allocation in case of a public health emergency.

Universal Health Coverage Index  Another health index that is used globally by the members of the United Nations (UN) is the universal health care (UHC) service coverage index. The UHC index monitors the country's progress related to the sustainable developmental goal (SDG) number three. The UHC service coverage index tracks 14 indicators related to reproductive, maternal, newborn, and child health, infectious diseases, non-communicable diseases, service capacity, and access to care. The main target of universal health coverage is to ensure that no one is denied access to essential medical services due to financial hardships. In 2021, the UHC index scores ranged from as low as 21 to a high score of 91 across 194 countries. 

In 2023, the health care system in Finland ranked first with a care index score of ****, followed by Belgium and Japan. Care systems index score is measured using multiple indicators from various public databases, it evaluates the capacity of a health system to treat and cure diseases and illnesses, once it is detected in the population This statistic shows the care systems ranking of countries worldwide in 2023, by their index score.

The healthcare ranking reflects the quality of health care and access to health services in different countries. The assessment includes various factors such as life expectancy, access to medical services, healthcare funding, and technologies.

According to a 2021 health care systems ranking among selected high-income countries, the United States came last in the overall ranking of its health care system performance. The overall ranking was based on five performance categories, including access to care, care process, administrative efficiency, equity, and health care outcomes. For the category administrative efficiency, which measures the amount of paperwork for providers and patients in the health system, the U.S. was ranked last, while Norway took first place. This could be because the health system in the U.S. is a multi-payer system, while Norway has a single-payer system, which most likely simplifies documentation and billing tasks. This statistic present the health care administrative efficiency rankings of the United States' health care system compared to ten other high-income countries in 2021.

About the Dataset

This dataset contains Quality of Life indices for various countries around the globe, extracted from the Numbeo website. The data provides valuable metrics for comparing countries based on several aspects of living standards, which can assist in decisions such as choosing a place to live or analyzing global trends in quality of life.

OBS: The code to generate this dataset is presented on: https://www.kaggle.com/code/marcelobatalhah/web-scrapping-quality-of-life-index

Columns in the Dataset

1. Rank:
   The global rank of the country based on its Quality of Life Index according to Year (1 = highest quality of life).

2. Country:
   The name of the country.

3. Quality of Life Index:
   A composite index that evaluates the overall quality of life in a country by combining other indices, such as Safety, Purchasing Power, and Health Care.

4. Purchasing Power Index:
   Measures the relative purchasing power of the average consumer in a country compared to New York City (baseline = 100).

5. Safety Index:
   Indicates the safety level of a country. A higher score suggests a safer environment.

6. Health Care Index:
   Evaluates the quality and accessibility of healthcare in the country.

7. Cost of Living Index:
   Measures the relative cost of living in a country compared to New York City (baseline = 100).

8. Property Price to Income Ratio:
   Compares the affordability of real estate by dividing the average property price by the average income.

9. Traffic Commute Time Index:
   Reflects the average time spent commuting due to traffic.

10. Pollution Index:
    Rates the level of pollution in the country (air, water, etc.).

11. Climate Index:
    Rates the favorability of the climate in the country (higher = more favorable).

12. Year:
    Year when the metrics were extracted.

Key Insights from the Dataset

• The Quality of Life Index aggregates multiple indicators, making it a useful single metric to compare countries.
• Specific indices such as Safety Index or Health Care Index allow for focused analysis on areas like security or healthcare quality.
• Cost of Living Index and Purchasing Power Index can help determine the affordability of living in each country.

How the Data Was Collected

• The dataset was built using web scraping techniques in Python.
• The data was extracted from the "Quality of Life Rankings by Country" page on Numbeo.
• Libraries used:
  • requests for retrieving webpage content.
  • BeautifulSoup for parsing the HTML and extracting relevant information.
  • pandas for organizing and storing the data in a structured format.

Possible Applications

1. Relocation Decision Making:
   Use the dataset to compare countries and identify destinations with high quality of life, safety, and healthcare.

2. Global Analysis:
   Perform exploratory data analysis (EDA) to identify trends and correlations across quality of life metrics.

3. Visualization:
   Plot global maps, bar charts, or other visualizations to better understand the data.

4. Predictive Modeling:
   Use this dataset as a base for machine learning tasks, like predicting Quality of Life Index based on other metrics.

The average for 2021 based on 186 countries was 1368.8 U.S. dollars. The highest value was in the USA: 11999.09 U.S. dollars and the lowest value was in Somalia: 14.63 U.S. dollars. The indicator is available from 2000 to 2023. Below is a chart for all countries where data are available.

In 2023, Norway ranked first with a health index score of 83, followed by Iceland and Sweden. The health index score is calculated by evaluating various indicators that assess the health of the population, and access to the services required to sustain good health, including health outcomes, health systems, sickness and risk factors, and mortality rates. The statistic shows the health and health systems ranking of European countries in 2023, by their health index score.

Data on the top universities for Medical and Health in 2025, including disciplines such as Medicine and Dentistry, and Other Health Subjects.

The average for 2021 based on 186 countries was 7.09 percent. The highest value was in Afghanistan: 21.51 percent and the lowest value was in Brunei: 2.15 percent. The indicator is available from 2000 to 2023. Below is a chart for all countries where data are available.

United States Index: NYSE Health Care data was reported at 15,506.530 31Dec2002=5000 in Oct 2018. This records a decrease from the previous number of 16,299.340 31Dec2002=5000 for Sep 2018. United States Index: NYSE Health Care data is updated monthly, averaging 7,026.580 31Dec2002=5000 from Dec 2002 (Median) to Oct 2018, with 191 observations. The data reached an all-time high of 16,299.340 31Dec2002=5000 in Sep 2018 and a record low of 4,504.980 31Dec2002=5000 in Feb 2009. United States Index: NYSE Health Care data remains active status in CEIC and is reported by New York Stock Exchange. The data is categorized under Global Database’s United States – Table US.Z001: NYSE: Indexes.

The average for 2021 based on 19 countries was 3215.44 U.S. dollars. The highest value was in the USA: 11999.09 U.S. dollars and the lowest value was in India: 75.55 U.S. dollars. The indicator is available from 2000 to 2023. Below is a chart for all countries where data are available.

About Dataset

Description This dataset contains detailed rankings and indicators from the 2023 Legatum Prosperity Index, assessing and ranking countries based on various dimensions of prosperity and development. The indicators cover aspects such as:

• Country: The name of the country.
• AveragScore: The overall average score of the country across all indicators.
• SafetySecurity: Freedom from conflict, terrorism, and crime.
• PersonelFreedom: Rights to speech, assembly, and individual autonomy.
• Governance: Quality of democracy, rule of law, and government effectiveness.
• SocialCapital: Strength of personal relationships and civic engagement.
• InvestmentEnvironment: Conditions for private investment and credit access.
• EnterpriseConditions: Business environment and market competition.
• MarketAccessInfrastructure: Ease of trade and quality of infrastructure.
• EconomicQuality: Macroeconomic stability and employment quality.
• LivingConditions: Standard of living and access to basic services.
• Health: Population health and healthcare access.
• Education: Quality and accessibility of education.
• NaturalEnvironment: Environmental quality and sustainability.

Source

The data is sourced from the 2023 Legatum Prosperity Index available at prosperity.com/rankings.

License

This dataset is shared under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). You are free to use, share, and adapt the data, provided that you attribute the source and share any derived works under the same license.

Acknowledgements

We acknowledge the Legatum Institute for compiling and providing the data used in this dataset. For more information on the methodology and detailed country reports, please visit the Legatum Prosperity Index website.

Usage

This dataset can be used for research, analysis, and educational purposes to understand the different dimensions of prosperity and development across countries in 2023. It is a valuable resource for policymakers, researchers, and anyone interested in global development metrics. Additionally, clustering analysis can be performed to group countries based on their development levels, providing insights into regional similarities and differences.

Keywords

Global Prosperity, Country Development, Safety, Governance, Health, Education, Economic Quality, 2023 Rankings, Legatum Prosperity Index, Clustering, Tabular, Social Science, Economics, Advanced

In 2025, South Africa had the highest health care index in Africa with a score of 63.8, followed by Kenya with 62 points. These scores, for both countries, are considered to be reasonably high. The health care index takes into account factors such as the overall quality of the health care system, health care professionals, equipment, staff, doctors, and cost.

Context:

Welcome to the "Milk, Cheese, and Eggs Prices Dataset" on Kaggle! This dataset provides comprehensive information about the prices of milk, cheese, and eggs across various countries in the year 2017. Understanding the pricing trends of these essential food items is crucial for economists, policymakers, and consumers alike, as they form the foundation of many diets worldwide. This dataset is a valuable resource for researchers, analysts, and anyone interested in studying global food pricing dynamics.

Content:

The dataset consists of the following columns:

1.**Countries**: This column represents the names of different countries included in the dataset. It is a categorical variable.

2.**Milk, Cheese, and Eggs Prices, 2017**: This column contains the average prices of milk, cheese, and eggs in each respective country for the year 2017. These prices are provided as numerical values, typically in the local currency per unit (e.g., per liter, per kilogram, etc.). This column is a numerical variable.

3.**Global Rank**: This column indicates the global ranking of each country based on the average prices of milk, cheese, and eggs in 2017. A lower rank suggests lower prices, while a higher rank indicates higher prices. This column is a numerical variable.

4.**Available Data**: This column informs users about the availability of data for each country. It can have binary values (e.g., "Yes" or "No"), indicating whether complete data is available for that specific country. This column is a categorical variable.

Column Features: 1.**Countries**: Categorical - Description: The names of countries included in the dataset. - Example: "United States," "France," "India."

2.**Milk, Cheese, and Eggs Prices, 2017**: Numerical - Description: The average prices of milk, cheese, and eggs in each respective country for the year 2017. - Example: 2.50 (indicating the average price in the local currency per unit).

3.**Global Rank**: Numerical - Description: The global ranking of each country based on the average prices of milk, cheese, and eggs in 2017. - Example: 5 (indicating the country's rank).

4.**Available Data**: Categorical - Description: Indicates whether complete data is available for a specific country or not. - Example: "Yes" or "No."

This dataset allows you to explore and analyze the pricing disparities of milk, cheese, and eggs across different countries, identify trends, and gain insights into the global food market. Researchers can use it for comparative studies, and policymakers can use it to inform decisions related to food affordability and accessibility.

Feel free to perform your analyses, build predictive models, or generate visualizations to extract meaningful insights from this dataset. We encourage you to share your findings with the Kaggle community and contribute to a better understanding of global food pricing dynamics.

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.

Data Source: This dataset was compiled from multiple data sources

If this was helpful, a vote is appreciated ❤️ Thank you 🙂

China Index: CSI 300 Index: Health Care data was reported at 8,514.350 31Dec2004=1000 in Nov 2025. This records a decrease from the previous number of 8,964.670 31Dec2004=1000 for Oct 2025. China Index: CSI 300 Index: Health Care data is updated monthly, averaging 7,882.270 31Dec2004=1000 from Jul 2007 (Median) to Nov 2025, with 221 observations. The data reached an all-time high of 18,614.674 31Dec2004=1000 in Jun 2021 and a record low of 2,139.340 31Dec2004=1000 in Oct 2008. China Index: CSI 300 Index: Health Care data remains active status in CEIC and is reported by China Securities Index Co., Ltd.. The data is categorized under China Premium Database’s Financial Market – Table CN.ZA: China Securities Index .

Quality of Life Index (higher is better) is an estimation of overall quality of life by using an empirical formula which takes into account purchasing power index (higher is better), pollution index (lower is better), house price to income ratio (lower is better), cost of living index (lower is better), safety index (higher is better), health care index (higher is better), traffic commute time index (lower is better) and climate index (higher is better).

Current formula (written in Java programming language):

index.main = Math.max(0, 100 + purchasingPowerInclRentIndex / 2.5 - (housePriceToIncomeRatio * 1.0) - costOfLivingIndex / 10 + safetyIndex / 2.0 + healthIndex / 2.5 - trafficTimeIndex / 2.0 - pollutionIndex * 2.0 / 3.0 + climateIndex / 3.0);

For details how purchasing power (including rent) index, pollution index, property price to income ratios, cost of living index, safety index, climate index, health index and traffic index are calculated please look up their respective pages.

Formulas used in the past

Formula used between June 2017 and Decembar 2017

We decided to decrease weight from costOfLivingIndex in this formula:

index.main = Math.max(0, 100 + purchasingPowerInclRentIndex / 2.5 - (housePriceToIncomeRatio * 1.0) - costOfLivingIndex / 5 + safetyIndex / 2.0 + healthIndex / 2.5 - trafficTimeIndex / 2.0 - pollutionIndex * 2.0 / 3.0 + climateIndex / 3.0);

The World Happiness 2017, which ranks 155 countries by their happiness levels, was released at the United Nations at an event celebrating International Day of Happiness on March 20th. The report continues to gain global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy-making decisions. Leading experts across fields – economics, psychology, survey analysis, national statistics, health, public policy and more – describe how measurements of well-being can be used effectively to assess the progress of nations. The reports review the state of happiness in the world today and show how the new science of happiness explains personal and national variations in happiness.

The scores are based on answers to the main life evaluation question asked in the poll. This question, known as the Cantril ladder, asks respondents to think of a ladder with the best possible life for them being a 10 and the worst possible life being a 0 and to rate their own current lives on that scale. The scores are from nationally representative samples for 2017 and use the Gallup weights to make the estimates representative. The columns following the happiness score estimate the extent to which each of six factors – economic production, social support, life expectancy, freedom, absence of corruption, and generosity – contribute to making life evaluations higher in each country than they are in Dystopia, a hypothetical country that has values equal to the world’s lowest national averages for each of the six factors. They have no impact on the total score reported for each country, but they do explain why some countries rank higher than others.

Quality of life index, link: https://www.numbeo.com/quality-of-life/indices_explained.jsp

Happiness store, link: https://www.kaggle.com/unsdsn/world-happiness/home

Introduction/Context: The term core value (CV) can be defined as fundamental beliefs or principles, guiding one's behavior in a social context. Though core competencies of family medicine (FM) have been clearly defined by WONCA, there has been an ongoing debate on what the CVs are for family doctors (FDs). Ukraine is a developing country in the middle of Europe with a population of 43 million inhabitants, gained independence from the Soviet Union in 1991. Ukraine is a low-income country, developing a modern European healthcare system, especially regarding FM. To implement WONCA standards, it is mandatory to assess the ongoing understanding of CVs in clinical daily practice among active FDs, working in different countries of Europe including Ukraine.Research questions: How do Ukrainian FDs (Delphi group experts) define the CVs of FM in Ukraine and how important are these CVs to a wider population of Ukrainian FDs in their everyday practice?Methods: A mixed method study was conducted in two steps during August and September 2020 in Ukraine. The first part was a qualitative Delphi round (three rounds) design among 20 Ukrainian FDs who were familiar with teaching and terms like CV. A consensus list of six CVs has emerged from the Delphi round study. The second part was a quantitative survey among Ukrainian FDs, who were not specially used to discussing CVs. The consensus list of those six CVs was then submitted to 2000 FDs (randomly selected) who were not involved in the Delphi team, to rank those values from one to nine, according to the importance from their personal point of view. Demographic characteristics have been assessed for all the participants of the Delphi round and quantitative survey.Results: Twenty FDs were involved as experts in the first Delphi round, whereas only five experts continued their participation in the second and the third rounds of the survey. The following six CVs emerged from the Delphi round: comprehensive approach, care coordination, first recourse, continuity of care, integrated approach, and patient and family centered care. The final sample consisted of 375 FDs (19% response rate). There were 323 (88.7%) female and 34 (9.3%) male FDs in the sample. The mean age of the participants was 44.6±13.5 years.Discussion/Conclusion: Defining CVs for FM by Ukrainian FDs in a given socio-economical and historical-cultural setting is crucial to optimize primary medical care and to guarantee an appropriate and successful implementation of WONCA standards as well as CVs in different countries including those where reformation of the health system is ongoing.

The global Ocean Health Index measures the state of the world’s oceans.The global OHI score for the 2024 assessment was 69, which was quite a bit lower than last year’s score of 73. This was due to COVID-related declines in tourism and recreation [the 2024 scores reflect 2021 data]. You can explore this and other goals using the interactive map which shows how different countries and goals contribute to the global score, as well as how the score has changed since 2012. Click on colored regions (i.e. EEZs) to see short country summaries.

Context

During COVID-19, different countries have put containment measures that include lockdowns and restrictive movement. Oxford University has done a research and created a numerical index which statistically measures the intensity of a restrictive measure.

Content

This is a time-series data of different countries representing measure of stringency on each day for the period of Jan-April 2020.

Acknowledgements

This dataset is a part of Oxford University research.

Inspiration

This data was used in the understanding of effect of countermeasures against the spread of COVID-19.