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 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 process, administrative efficiency, equity, and health care outcomes. For the category access to care, which measures affordability and timeliness of health care, the U.S. also ranked last, whilst the Netherlands took first place. This statistic illustrates the access to care rankings of the United States' health care system compared to ten other high-income countries in 2021.

According to a survey from *************, Taiwan was ranked as the best country for expat healthcare, followed by South Korea and Qatar. This statistic represents the ranking of top ten countries with best healthcare for expats worldwide in 2023.

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.

Global Health Spending

Country Global Health Spending Over Time

By Eva Murray [source]

About this dataset

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

To get started with this data, begin by exploring the location and time columns as these will provide a breakdown of which countries are represented in the dataset as well as when each observation was collected. To drill down further into the analysis, use indicators, subjects and measures fields for comparison between healthcare spending for different topics like drug access or acute care across countries over time. The values field contains actual values related to healthcare spending while flag codes tell you if there are any discrepancies in data quality so it is important look into those too if necessary.

This dataset is useful for research relatedto how global health expenditures have varied across different countries over time and difference sources of funding among a few other applications. Understanding what's included in this dataset will help you determine how best to use it when doing comparative country-level analyses or international studies on healthcare funding sources over time

Research Ideas

• Identify countries with high public health spending as a percentage of GDP and determine if their population has better health outcomes than those with lower spending.
• Compare public health investments across various countries during the same period to ascertain areas that need more attention, such as medical research, vaccinations, medication and healthcare staffing.
• Determine the trends in health expenditures over time for key indicators such as life expectancy to gain insights into how well a country is managing its healthcare sector

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: DP_LIVE_18102020154144776.csv | Column name | Description | |:---------------|:-----------------------------------------| | LOCATION | Country or region of the data. (String) | | INDICATOR | Health spending indicator. (String) | | SUBJECT | Health spending subject. (String) | | MEASURE | Measurement of health spending. (String) | | FREQUENCY | Frequency of data collection. (String) | | TIME | Year of data collection. (Integer) | | Value | Value of health spending. (Float) | | Flag Codes | Codes related to data quality. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Eva Murray.

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.

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.

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

This dataset provides a comparative analysis of education and health indicators across top countries, including Poland, Finland, Italy, and the USA etc... The data covers a range of indicators related to education, such as literacy rates, enrollment rates, and education spending, as well as health indicators such as life expectancy, infant mortality rates, and healthcare spending. The data is sourced from various official and publicly available data sources, including the World Bank, the United Nations, and country-specific government websites. Researchers, analysts, and educators can use this dataset to gain insights into the education and health outcomes of different countries, as well as to identify areas for improvement and best practices. The dataset is ideal for cross-country comparative analysis and can be used to inform policy-making, research, and educational programs.

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.

This table provides statistics on the Top 50 Health Service Codes for services provided to Alberta Residents in Other Countries (Except United States). This table is an Excel version of a table in the “Alberta Health Care Insurance Plan Statistical Supplement” report published annually by Alberta Health.

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

In 2024, ** percent of adults worldwide agreed that many people in their country could not afford good healthcare. Individuals in Brazil were most likely to agree with this statement "Many people in my country cannot afford good healthcare.", while the least share of individuals agreed in Sweden. The results generally reflect the wealth of a nation, with people from wealthier countries tending to agree that good healthcare is affordable. The biggest exception being the U.S. where over ********* of U.S. respondents agreed that good health care is unaffordable to many despite being one of the richest country in the world. This statistic shows the percentage of adults in select countries worldwide who agreed that many people in their country could not afford good healthcare as of 2024.

Large discrepancies exist between standards of healthcare provision in high-income (HICs) and low and middle-income countries (LMICs). The root cause is often financial, resulting in poor infrastructure and under-resourced education and healthcare systems. Continuing professional education (CPE) programmes improve staff knowledge, skills, retention, and practice, but remain costly and rare in low-resource settings. One potential solution involves healthcare education collaborations between institutions in HICs and LMICs to provide culturally appropriate CPE in LMICs. To be effective, educational partnerships must address the challenges arising from differences in cultural norms, language, available technology and organisational structures within collaborating countries. Seven databases and other sources were systematically searched on 7 July 2020 for relevant studies. Citations, abstracts, and studies were screened and consensus was reached on which to include within the review. 54 studies were assessed regarding the type of educational programme involved, the nature of HIC/LMIC collaboration and quality of the study design. Studies varied greatly regarding the types and numbers of healthcare professionals involved, pedagogical and delivery methods, and the ways in which collaboration was undertaken. Barriers and enablers of collaboration were identified and discussed. The key findings were: 1. The methodological quality of reporting in the studies was generally poor. 2. The way in which HIC/LMIC healthcare education collaboration is undertaken varies according to many factors, including what is to be delivered, the learner group, the context, and the resources available. 3. Western bias was a major barrier. 4. The key to developing successful collaborations was the quality, nature, and duration of the relationships between those involved. This review provides insights into factors that underpin successful HIC/LMIC healthcare CPE collaborations and outlines inequities and quality issues in reporting.

This horizontal bar chart displays health expenditure (% of GDP) by country using the aggregation average, weighted by gdp in Norway. The data is filtered where the date is 2021. The data is about countries per year.

Context

In order to begin correlating global data based around infection rates, from the WHO data in the UNCOVER: Covid-19 challenge, found here, to quality of healthcare in a region, data relaying the availability of health care in nations around the globe is necessary as a first step to this analysis. Out of a general desire to provide this data to the data science community, and out of a desire to find ways to learn about, prepare for in whatever way possible, and beat, the COVID-19 pandemic of 2020, I'm making this data-set public for others to use, share, and study with.

Content

The data presented in the file below cover the following information... 1 set of Strings --> The country names 1 set of Integers --> The years in which the data were recorded (2010-2014). 6 sets of floats --> 6 columns of floats record the total density of health centers and hospitals (including provincial and specialized) to every 100,000 people within the country... thus generalizing the country's access to health care, and maintenance/creation of the health infrastructure needed to support the population.

Acknowledgements

Complete thanks for this data-set goes to the World Health Organization and the Global Health Observatory. This data can be found on the GHO's site, specifically here. In terms of the licensing, in order to underscore that this data is not mine, as well as ensure all steps are taken to make one's proper rights clear (and grant thanks for the data once again), the general data usage license agreement for the data-set used can be found here.

Inspiration

It is sadly true that this data on its own is unlikely to present any major answers. When combined with other datasets however, this may yield answers as to what factors of a countries existence may indicate its ability to maintain a large health infrastructure. In fact, determining how a country's finances, natural resource list (as just ideas), etc. relate to a country's ability to sustain a decent health infrastructure would be an extremely interesting question to answer. I hope you may find the data helpful in your endeavors!

Disclaimer: This is my first ever published data-set on Kaggle. While I've done my best to ensure it's fairly descriptive for any potential visitors, please do feel free to leave any comments you may have in the discussions section! I'm always open to finding ways to improve.

WHO Health Indicators for Algeria

Healthcare Delivery, Socioeconomic and Demographic Statistics

By Humanitarian Data Exchange [source]

About this dataset

This dataset from the World Health Organization (WHO) contains comprehensive data on various health indicators for Algeria. It covers various topics such as mortality, sustainable development, global health estimates, health systems, malaria and tuberculosis, child and infection diseases, public health and environment, substance use and mental health tobacco injuries and violence HIV/AIDS nutrition urban Health noncommunicable diseases financial protection medical equipment demographic socioeconomic statistics essential health technologies medical equipment insecticide resistance oral Health universal healthcare global observatory for eHealth human resources information systems youth AMR glass noncommunicable diseases mental healthcare workforce neglected tropical diseases AMR GASP ICD sexual reproductive care and many more.

It provides resource descriptions that allow users to access individual indicator metadata as well as detailed coverage on different countries in the world. The dataset also includes methods related to registry interlinked with last updated information from WHO’s data portal and license terms provided under a variety of other sources.

The analysis of this dataset allows us to know more about the state of public healthcare in Algeria which can eventually lead us not only to an improved understanding but also better initiatives that are designed to benefit the wellbeing of citizens across this nation

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains a wide variety of health-related indicators for Algeria from the World Health Organization’s (WHO) global health portal. This data can be used to gain insights into various socio-economic conditions, health systems, and public policy strategies in Algeria.

Getting Started

Firstly, you should download the dataset from Kaggle and unzip it into a folder of your choice. Then open the .csv file with your favorite spreadsheet or text editor application. After you have opened the dataset file, you will be able to see all of the available categories and indicator variables included in this dataset.

Understanding The Dataset

The columns in this dataset are divided into two categories; GHO (Global Health Observatory) metadata fields and variable fields describing each indicator value. The GHO metadata fields provide contextual information on where each individual healthcare indicator was sourced from including its reference year(s), geographic region/country, data source code/url and publication state code/url among others. These types of fields can be helpful when interpreting more specific results related to an entire given region or country for example. The second category includes variable fields that contain individual healthcare indicators such as mortality rates or access to clean water for example related to a specific region or population group within Algeria as well as their corresponding statistical values such as low & high values collected over a period of time etc.. Additionally it is important to note that columns with ** after them indicate labels which are relevant only if applicable e..g Low**

Best Practices For Analysing Data

When analysing this type of data consider which comparison type(s) would work best given your end goal: absolute comparison between 2+ geographies over same timeframe? Two periods compared comparatively within same geography? Or different measurements all using same base geography (ie one country)? Once you decide what type of analysis makes sense then use applicable filters/areas such as regions , provinces etc & start slicing up datasets according to whatever measure works best until desired outcomes are found e..g filter out by age groups / sex / marital status / ethnicity etc rather than downloading entire table with all stats together thereby simplifying efforts & narrowing down scope greatly improving accuracy along way whilst identifying potential must know trends quickly through visualisations generated Charts / tables when combined often highlight underlying relationships quickly which is key before analysing further Deep diving combined datasets by cross referencing various indices allowing viewers gain even better insights specially when combined structured narrative explanations composed backed up by facts

Research Ideas

• Creating visualizations that show changes in health indicators over tim...