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.

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.

BackgroundssssHealth system performance is a multifaceted concept that encompasses various dimensions of a nation’s healthcare infrastructure. This study aims to assess and rank the performance of health systems across different regions of the world.MethodologyWe employed the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method in 2023 to evaluate and rank the health system performance of 31 countries across six geographical regions. Our evaluation included six general categories and twelve indicators related to health, finance, and the COVID-19 pandemic. The final weights for these indicators were determined using the Three-scale method and the Entropy-weighting method. Additionally, we categorized health system performance into three groups: high, moderate, and low. Hierarchical clustering of health system performance scores was conducted using SPSS software (version 26).ResultsLuxembourg emerged as the only high-performing health system, while Qatar and the Netherlands fell into the moderate-performance group. Other countries exhibited low-performing health systems. Notably, within the low-performance group, the United States of America, Australia, Singapore, Canada, England, and Germany achieved relatively better rankings. Conversely, Yemen, Egypt, Afghanistan, and Bolivia ranked lowest in terms of health system performance.ConclusionContrary to the assumption that higher health spending guarantees improved performance, the experience of COVID-19 among high-income countries revealed mixed results. Strengthening resilience, investing in public health systems, and ensuring sustainable financial resources are crucial for enhancing health system performance.

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.

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.

Global health system performance rankings of the 31 countries.

Countries by Gross National Income (GNI)

Economic health by nation

About this dataset

Gross National Income (GNI) is a marker of the economic health of a nation - it encompasses a nation's GDP while also taking into account money flowing in and out of the country from foreign trade. This dataset provides GNI rankings for countries around the world, allowing for comparisons of economic health and growth. Explore how different nations fare in terms of GNI, and what this says about their overall economic stability!

How to use the dataset

The Gross National Income (GNI) of countries around the world is a measure of the economic health of a nation. It is a summation of a nation's GDP (Gross Domestic Product) plus the money flowing into and out of the country from foreign countries.

This dataset provides Rankings of countries by their GNI. The data is divided into two files: df_1.csv and df_2.csv. Both files contain the following columns:

No.: The number of the country. (Numeric)

Country: The name of the country. (String)

Research Ideas

• Measuring the economic health of a nation
• Comparing the GDP of different countries
• Determining the money flow into and out of a country

Acknowledgements

GNI data is sourced from wikipedia

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: df_1.csv

File: df_4.csv | Column name | Description | |:----------------------------|:----------------------------------------------------------------------| | No. | The rank of the country based on GNI. (Numeric) | | Country | The name of the country. (String) | | GNI (Atlas method)[8] | The GNI of the country, in US dollars. (Numeric) | | GNI (Atlas method)[8].1 | The GNI of the country, as a percentage of the world total. (Numeric) | | GNI[9] | The GNI of the country, in US dollars. (Numeric) | | GNI[9].1 | The GNI of the country, as a percentage of the world total. (Numeric) | | GDP[10] | The GDP of the country, in US dollars. (Numeric) |

File: df_9.csv | Column name | Description | |:--------------|:----------------------| | 0 | Country Name (String) | | 1 | GNI (Integer) |

File: df_3.csv | Column name | Description | |:--------------|:----------------------| | 0 | Country Name (String) |

File: df_2.csv

File: df_6.csv | Column name | Description | |:--------------|:------------------------------------------------------------------| | Rank | The rank of the country based on GNI. (Numeric) | | 2021 | The GNI of the country in 2021. (Numeric) | | 2021.1 | The GNI of the country in 2021, adjusted for inflation. (Numeric) | | 2016 | The GNI of the country in 2016. (Numeric) | | 2016.1 | The GNI of the country in 2016, adjusted for inflation. (Numeric) | | 2014 | The GNI of the country in 2014. (Numeric) | | 2014.1 | The GNI of the country in 2014, adjusted for inflation. (Numeric) | | 2013 | The GNI of the country in 2013. (Numeric) | | 2013.1 | The GNI of the country in 2013, adjusted for inflation. (Numeric) | | 2012 | The GNI of the country in 2012. (Numeric) | | 2012.1 | The GNI of the country in 2012, adjusted for inflation. (Numeric) | | 2011 | The GNI of the country in 2011. (Numeric) | | 2011.1 | The GNI of the country in 2011, adjusted for inflation. (Numeric) | | 2010 | The GNI of the country in 2010. (Numeric) | | 2010.1 | The GNI of the country in 2010, adjusted for inflation. (Numeric) | | 2009 | The GNI of the country in 2009. (Numeric) | | 2009.1 | The GNI of the country in 2009, adjusted for inflation. (Numeric) | | 2008 | The GNI of the country in 2008. (Numeric) | | 2008.1 | The GNI of the country in 200...

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.

Ranking of 31 countries (Out of Pocket Spending & Prepaid Private Spending Indices).

In 2014, Government Expenditure on Health in Monaco rose 18.8% compared to the previous year.

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.

Ranking of 31 countries (Deaths of COVID-19 & Vaccinations of COVID-19).

Top 10 most productive countries, institutions and authors of health literacy.

The average for 2021 based on 17 countries was 65.06 index points. The highest value was in Costa Rica: 128.98 index points and the lowest value was in Nicaragua: 28.86 index points. The indicator is available from 2017 to 2021. Below is a chart for all countries where data are available.

The County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure the health of nearly all counties in the nation and rank them within states. This feature layer contains 2020 County Health Rankings data for nation, state, and county levels. The Rankings are compiled using county-level measures from a variety of national and state data sources. Some example measures are:adult smokingphysical inactivityflu vaccinationschild povertydriving alone to workTo see a full list of variables, as well as their definitions and descriptions, explore the Fields information by clicking the Data tab here in the Item Details. These measures are standardized and combined using scientifically-informed weights."By ranking the health of nearly every county in the nation, County Health Rankings & Roadmaps (CHR&R) illustrates how where we live affects how well and how long we live. CHR&R also shows what each of us can do to create healthier places to live, learn, work, and play – for everyone."Some new features of the 2020 Rankings data compared to previous versions:More race/ethnicity categories, including Asian/Pacific Islander and American Indian/Alaska NativeReliability flags that to flag an estimate as unreliable5 new variables: math scores, reading scores, juvenile arrests, suicides, and traffic volumeData Processing Notes:Data downloaded March 2020Slight modifications made to the source data are as follows:The string " raw value" was removed from field labels/aliases so that auto-generated legends and pop-ups would only have the measure's name, not "(measure's name) raw value" and strings such as "(%)", "rate", or "per 100,000" were added depending on the type of measure.Percentage and Prevalence fields were multiplied by 100 to make them easier to work with in the map.For demographic variables only, the word "numerator" was removed and the word "population" was added where appropriate.Fields dropped from analytic data file: yearall fields ending in "_cihigh" and "_cilow"and any variables that are not listed in the sources and years documentation.Analytic data file was then merged with state-specific ranking files so that all county rankings and subrankings are included in this layer.