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. 

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.

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.

According to a 2021 health care systems ranking among selected high-income countries, the U.S. 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 processes, administrative efficiency, equity, and health care outcomes. Among the top ranked countries were Norway, the Netherlands, and Australia, while Switzerland, Canada, and the United States were among the lowest ranked.

Administrative efficiency and costs Generally, in countries like Norway, Australia, and New Zealand, where a single-payer health system is in place, there is higher administrative efficiency and lower health administrative costs. The U.S. with its multi-payer system, on the other hand, generates extra bureaucratic tasks for both health care providers and the patients. In the U.S. an estimated 256 million U.S. dollars are wasted per year due to administrative complexity. Equity The United States, without universal health coverage, has expectedly large disparities in health care affordability based on income, as individuals with low income are often uninsured and must pay for all their health care out-of-pocket. These results are in line with the equity rankings of this report where the U.S. also came last. With the performance category equity, it is important to point out that the report focuses on income-related disparities. Other disparities based on ethnicity, gender, geography, and more have not been taken into consideration.

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.

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.

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.

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.

Cost of Living - Country Rankings Dataset

Context:

The "Cost of Living - Country Rankings Dataset" provides comprehensive information on the cost of living in various countries around the world. Understanding the cost of living is crucial for individuals, businesses, and policymakers alike, as it impacts decisions related to travel, relocation, investment, and economic analysis. This dataset is intended to serve as a valuable resource for researchers, data analysts, and anyone interested in exploring and comparing the cost of living across different nations.

Content:

This dataset comprises four primary columns:

1. Countries: This column contains the names of various countries included in the dataset. Each country is identified by its official name.

2. Cost of Living: The "Cost of Living" column represents the cost of living index or score for each country. This index is typically calculated by considering various factors, such as housing, food, transportation, healthcare, and other essential expenses. A higher index value indicates a higher cost of living in that particular country, while a lower value suggests a more affordable cost of living.

3. 2017 Global Rank: This column provides the global ranking of each country's cost of living in the year 2017. The ranking is based on the cost of living index mentioned earlier. A lower rank indicates a lower cost of living relative to other countries, while a higher rank suggests a higher cost of living position.

4. Available Data: The "Available Data" column indicates whether or not data for a specific country and year is available.

This dataset is designed to support various data analysis and visualization tasks. Users can explore trends in the cost of living, identify countries with high or low cost of living, and analyze how rankings have changed over time. Researchers can use this dataset to conduct in-depth studies on the factors influencing the cost of living in different regions and the economic implications of such variations.

Please note that the dataset includes information for the year 2017, and users are encouraged to consider this when interpreting the data, as economic conditions and the cost of living may have changed since then. Additionally, this dataset aims to provide a snapshot of cost of living rankings for countries in 2017 and may not cover every country in the world.

Link: https://www.theglobaleconomy.com/rankings/cost_of_living_wb/

Disclaimer: The accuracy and completeness of the data provided in this dataset are subject to the source from which it was obtained. Users are advised to cross-reference this data with authoritative sources and exercise discretion when making decisions based on it. The dataset creator and Kaggle assume no responsibility for any actions taken based on the information provided herein.

Context:

Understanding and analyzing healthcare prices globally is essential for making informed decisions in healthcare policy, medical tourism, and personal healthcare choices. In this Kaggle dataset, we aim to shed light on healthcare prices in different countries in 2017. To get started, let's define the columns and features within the dataset and discuss how we can work with them effectively.

Content:

Introduction - Introduce the dataset and its importance in understanding global healthcare pricing. - Explain the objective of this analysis.

Columns:

a. Countries: This column contains the names of different countries.

b. Healthcare Prices, 2017: This column represents healthcare prices in the respective countries for the year 2017. It serves as the primary numerical feature of interest.

c. Global Rank: This column provides the global rank of each country based on its healthcare prices in 2017. It can be used for comparative analysis and benchmarking.

d. Available Data: This column likely indicates whether there is complete data available for each country in the dataset. It may be binary (e.g., 'Yes' or 'No').

Features: - Discuss what features are and how they relate to columns. Features are variables used in analysis, and in this dataset, the primary feature is "Healthcare Prices, 2017."

Data Preprocessing - Check for missing values in the dataset, especially in the "Healthcare Prices, 2017" column. - Deal with any outliers that may skew the analysis. - If the "Available Data" column contains relevant information, explain how it might be used or whether it needs preprocessing.

Exploratory Data Analysis (EDA) - Visualize the distribution of healthcare prices in 2017. - Explore any patterns or anomalies in the data. - Consider using statistical measures like mean, median, and standard deviation to summarize the data.

Feature Engineering - If necessary, discuss potential feature engineering techniques. For example, you might create new features like "Healthcare Prices Growth Rate" or "Healthcare Prices Quartiles."

Working with Missing Data (if applicable) - If there are missing data points in the "Available Data" column, discuss strategies for handling this missing information, such as imputation or exclusion.

Conclusion - Summarize the main points discussed in this document. - Emphasize the importance of understanding the dataset's columns and features before diving into a more detailed analysis.

Global health system performance rankings of the 31 countries.

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 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.

The average for 2021 based on 45 countries was 44.61 index points. The highest value was in South Africa: 76.27 index points and the lowest value was in Sudan: 17.84 index points. The indicator is available from 2017 to 2021. Below is a chart for all countries where data are available.

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...

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 (Out of Pocket Spending & Prepaid Private Spending Indices).

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.

Full ranking of countries’ healthcare capacity response capacity and resilience.