Global Health Spending

Country Global Health Spending Over Time

By Eva Murray [source]

About this dataset

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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.

Dataset Overview

This dataset presents a focused snapshot of Primary Health Care (PHC) Expenditure per Capita across 114 countries. The data spans from 2016 to 2022, though not all years are represented for each country. It reflects the financial commitment of nations to primary health care, providing a basis for comparative analysis of health spending priorities and trends over time.

Data Science Applications

Despite its modest size, this dataset is ripe for exploratory data analysis, trend analysis, and cross-country comparisons. It can be used to model health expenditure growth, forecast future spending, and identify outliers. Data scientists can also merge it with other datasets to study correlations between PHC expenditure and health outcomes or economic indicators.

Column Descriptors

• Countries: The nation to which the data pertains.
• Indicators: Specifies the type of data, here it's PHC Expenditure per Capita.
• 2016 - 2022: Yearly expenditure data in US dollars. Note that not all countries have data for each year.

Ethically Collected Data

The data was sourced from the WHO's publicly available Global Health Expenditure Database, ensuring ethical collection and sharing practices. It adheres to international standards for health data transparency and accessibility.

Acknowledgements

I extend my gratitude to the United Nations and its specialized agencies for compiling and maintaining the health expenditure data and to Dall E3 for enhancing my dataset presentation with relevant imagery.

The Global Health Expenditure Database (GHED) provides internationally comparable data on health spending for close to 190 countries. The database is open access and supports the goal of Universal Health Coverage by helping monitor the availability of resources for health and the extent to which they are used efficiently and equitably. This, in turn, helps ensure health services are available and affordable when people need them...WHO works collaboratively with Member States and updates the database annually using available data such as government budgets and health accounts studies. Where necessary, modifications and estimates are made to ensure the comprehensiveness and consistency of the data across countries and years. GHED is the source of the health expenditure data republished by the World Bank and the WHO Global Health Observatory. (from website)

The Global Health Expenditure Database (GHED) provides comparable data on health expenditure for 194 countries and territories since 2000 with open access to the public. Health spending indicators are key guides for monitoring the flow of resources, informing health policy development, and promoting the transparency and accountability of health systems. The database can help to answer questions, such as how much countries and territories spend on health, how much of the health spending comes from government, households, and donors, and how much of the spending is channeled through compulsory and voluntary health financing arrangements. The database also includes a detailed breakdown of spending for an increasing number of countries and territories on health care functions and primary health care, spending by diseases and conditions, spending for the under 5-year-old population, and spending by provider type. Information on health capital investments is also included.

Context

This dataset simply combines publicly available data to characterise a country based on healthcare factors, economy, government and demographics.

Content

All data are given per 100.000 inhabitants where this is appropriate scores are given as absolute values and so are spending and demographics. Each row represents one country. Data that is included covers the following topics:

Healthcare: - Staff including: Nurses and Physicians per 100.000 inhabitants - Infrastructure including: Beds, Chnage of beds between 2018 and 2019 and the change of bed numbers since 2013, Intensive Care Unit (ICU) beds, ventilators and Extra Corporal Membrane Oxygenation (ECMO), machines per 100.000 inhabitants - Total spending on healthcare in US dollars per capita.

Demographics: - The median age for entire population and each gender - The percentage of the population within age brackets - Total population - Population per km2 - Population change between 2018 and 2019

Government The used scores are from the Economist intelligence unit and describe how democratic a country is and how the government works. These can be used to compare countries based on their government type.

Acknowledgements

All data is publicly available and just has been brought together in one place. The sources are:

• Population densities

• Populations

• ECMO machines

• Hospital beds

• Number of physicians

• Health expenditure per capita

• Democracy indexes

• Median age

• OECD nurses per capita

Inspiration

These data are meant as metadata to decide which countries are comparable. I am working on healthcare data so the inspiration is to compare health statistics between countries and make an informed decision about how comparable they are. Could be used for any non healthcare related task as well.

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 🙂

The rationale for developing the EU HCCD for use in Health Technology Assessment (HTA) across countries is to provide a common dataset of international costs, which can feed into health economic evaluations carried out by transferring economic evaluation analysis and models across countries. Defining a core dataset of costs for use in HTA across countries enables analyses that try to understand the variation in costs within and across countries (taking into account the differences between the healthcare systems and other factors). Additionally, it makes it easier to carry out multi-country studies and to adapt economic evaluation studies from country to country by saving human resources time (and consequently costs) in the task of looking for healthcare costs.

Household out-of-pocket payment’ means a direct payment for healthcare goods and services from the household primary income or savings, where the payment is made by the user at the time of the purchase of goods or the use of the services. Data are collected according to Commission Regulation (EC) 2015/359 as regards statistics on healthcare expenditure and financing (System of Health Accounts 2011 manual).

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.

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.

Read me

Pharmaceutical Drug Spending by countries with indicators such as a share of total health spending, in USD per capita (using economy-wide PPPs) and as a share of GDP. Plus, total spending by each countries in the specific year.

Data

Data comes from Organisation for Economic Cooperation and Development on https://data.oecd.org/healthres/pharmaceutical-spending.htm

It consists of useful information about percent of health spending, percent of GDP and US dollars per capita for specific countries. Also, we added total spending by countries using their population data.

Population data comes from DataHub http://datahub.io/core/population since it is regularly updated and includes all country codes.

Preparation

There are several steps have been done to get final data.

We extracted separately each resource by “percent of health spending”, “percent of GDP” and “US dollars per capita” We merged them into one resource and added new column “TOTAL_SPEND” “TOTAL_SPEND” is calculated using “US dollars per capita” and “population” data. Source for original pharmacy drug spending: https://stats.oecd.org/sdmx-json/data/DP_LIVE/.PHARMAEXP.../OECD?contentType=csv&detail=code&separator=comma&csv-lang=en.

Public Domain Dedication and License (PDDL)

Credits

This dataset was from tekkum and the original file was in xlsx format.

Context

While numerous studies have explored the factors influencing life expectancy, most have focused on demographic variables, economic indicators, and mortality rates. However, there has been limited examination of the impact of immunization coverage, health expenditures, and educational attainment on life expectancy. This study seeks to address these gaps by developing a comprehensive dataset with no missing values analyses, utilizing data from many years across 193 different countries. Key immunizations such as Hepatitis B, Polio, and Diphtheria, along with factors like GDP, schooling, and health expenditure, are included in this dataset. This approach aims to identify the most significant predictors of life expectancy, allowing countries to prioritize interventions that could most effectively improve the health and longevity of their populations.

Dataset

The success of this analysis relies heavily on the accuracy and completeness of the data. The dataset used in this project has been sourced from the Global Health Observatory (GHO) data repository of the World Health Organization (WHO), which tracks health metrics and related factors for countries worldwide. The corresponding economic data was obtained from the United Nations. From the broad range of health-related variables available, this study focuses on those that are most representative and critical to understanding life expectancy. The dataset includes data for 193 countries and has been meticulously merged into a single file containing 22 columns and 2,938 rows, representing 20 predictive variables. The variables were categorized into four main groups: Immunization-related factors, Mortality factors, Economic factors, and Social factors. Countries with a lot of missing values were excluded, and some values were generated by Bayesian Ridge.

Question

This dataset aims to answer the following key questions:

1. Do the selected predictive factors significantly impact life expectancy, and which variables are the most influential?

2. Should countries with a lower life expectancy (below 65 years) increase healthcare expenditure to improve their population's lifespan?

3. How do infant and adult mortality rates influence life expectancy across different regions?

4. What is the relationship between life expectancy and lifestyle factors such as alcohol consumption?

5. How does educational attainment, as measured by years of schooling, affect human lifespan?

6. Is there a positive or negative correlation between alcohol consumption and life expectancy?

7. What is the impact of immunization coverage on life expectancy, particularly regarding diseases like Hepatitis B, Polio, and Diphtheria?

Context

There can be multiple motivations for analyzing country specific data, ranging from identifying successful approaches in healthcare policy to identifying business investment opportunities, and many more. Often, all these various goals would have to analyze a substantially overlapping set of parameters. Thus, it would be very good to have a broad set of country specific indicators at one place.

This data-set is an effort in that direction. Of-course there are still plenty more parameters out there. If anyone is interested to integrate more parameters to this dataset, you are more than welcome.

Content

This dataset contains about 95 statistical indicators of the 66 countries. It covers a broad spectrum of areas including

General Information Broader Economic Indicators Social Indicators Environmental & Infrastructure Indicators Military Spending Healthcare Indicators Trade Related Indicators e.t.c.

This data-set for the year 2017 is an amalgamation of data from SRK's Country Statistics - UNData, Numbeo and World Bank.

The entire data-set is contained in one file described below:

soci_econ_country_profiles.csv - The first column contains the country names followed by 95 columns containing the various indicator variables.

Acknowledgements

This is a data-set built on top of SRK's Country Statistics - UNData which was primarily sourced from UNData.

Additional data such as "Cost of living index", "Property price index", "Quality of life index" have been extracted from Numbeo and a number of metrics related to "trade", "healthcare", "military spending", "taxes" etc are extracted from World Bank data source. Given that this is an amalgamation of data from three different sources, only those countries(about 66) which have sufficient data across all the three sources are considered.

Please read the Numbeo terms of use and policieshere Please read the WorldBank terms of use and policies here Please read the UN terms of use and policies here

Photo Credits : Louis Maniquet on Unsplash

• I've downloaded and wrangled the John Hopkins dataset on the number of reported COVID-19 cases by country.
• I joined this dataset with a bunch of additional metadata on country-level.

The data only covers the period Jan. 22 - March 23, but it should be a piece of cake to apply the metadata provided here on a larger range of data (just perform a join operation).

I used the dataset for an online lecture on data visualization --> https://www.youtube.com/watch?v=l85l1qmosEU

The additional variables provided here could shed some light on correlational relations between - for example - the share of government expenditure in the health care system and the growth rate of the virus in a given country.

• Data sources:

--> Reported COVID-19 cases by country by day: https://github.com/CSSEGISandData/COVID-19 --> Data on health expenditure comes from WHO: https://apps.who.int/nha/database/Select/Indicators/en (created my own table) --> Population data and other socio-demographic data: https://www.worldometers.info/world-population/population-by-country/ --> Countries divided by continent: https://www.worldometers.info/geography/7-continents/

• Methodology:

1. John Hopkins data was sometimes inconsistent in their level of aggregation; reporting country-level data in most cases, but sometimes opting for county/state level data (e.g. for the United States). I aggregated all data to country-level. Moreover, there were plenty of inconsistencies in country notation between the different datasets, which required some fuzzy matching techniques.
2. I melted the data so that one line corresponds with one day in a particular country. This is the default data scheme for anyone trying to do some data viz on the dataset at hand (I sacrifice data size for convenience here, I know...)

Some of the interactive dashboards created with this data:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2342187%2F2d8e73336e269038f06b43f81183fd87%2Fcovid19%20dashboard.JPG?generation=1597334049308430&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2342187%2Ff08488ae7bded1f5850e730b87437782%2Fcovid19%20dashboard%202.JPG?generation=1597334070819173&alt=media" alt="">

Have fun!

Background: Rising expenditure for new cancer medicines is accelerating concerns that their costs will become unsustainable for universal healthcare access. Moreover, early market access of new oncology medicines lacking appropriate clinical evaluation generates uncertainty over their cost-effectiveness and increases expenditure for unknown health gain. Patient-level data can complement clinical trials and generate better evidence on the effectiveness, safety and outcomes of these new medicines in routine care. This can support policy decisions including funding. Consequently, there is a need for improving datasets for establishing real-world outcomes of newly launched oncology medicines.Aim: To outline the types of available datasets for collecting patient-level data for oncology among different European countries. Additionally, to highlight concerns regarding the use and availability of such data from a health authority perspective as well as possibilities for cross-national collaboration to improve data collection and inform decision-making.Methods: A mixed methods approach was undertaken through a cross-sectional questionnaire followed-up by a focus group discussion. Participants were selected by purposive sampling to represent stakeholders across different European countries and healthcare settings. Descriptive statistics were used to analyze quantifiable questions, whilst content analysis was employed for open-ended questions.Results: 25 respondents across 18 European countries provided their insights on the types of datasets collecting oncology data, including hospital records, cancer, prescription and medicine registers. The most available is expenditure data whilst data concerning effectiveness, safety and outcomes is less available, and there are concerns with data validity. A major constraint to data collection is the lack of comprehensive registries and limited data on effectiveness, safety and outcomes of new medicines. Data ownership limits data accessibility as well as possibilities for linkage, and data collection is time-consuming, necessitating dedicated staff and better systems to facilitate the process. Cross-national collaboration is challenging but the engagement of multiple stakeholders is a key step to reach common goals through research.Conclusion: This study acts as a starting point for future research on patient-level databases for oncology across Europe. Future recommendations will require continued engagement in research, building on current initiatives and involving multiple stakeholders to establish guidelines and commitments for transparency and data sharing.

This dataset offers a detailed comparison of key global players like USA, Russia, China, India, Canada, Australia, and others across various economic, social, and environmental metrics. By comparing countries on indicators such as GDP, population, healthcare access, education levels, internet penetration, military spending, and much more, this dataset provides valuable insights for researchers, policymakers, and analysts.

🔍 Key Comparisons:

Economic Indicators: GDP, inflation rates, unemployment rates, etc. Social Indicators: Literacy rates, healthcare quality, life expectancy, etc. Environmental Indicators: CO2 emissions, renewable energy usage, protected areas, etc. Technological Advancements: Internet users, mobile subscriptions, tech exports, etc. Military Spending: Defense budgets, military personnel numbers, etc. This dataset is perfect for those who want to compare countries in terms of development, growth, and global standing. It can be used for data analysis, policy planning, research, and even education.

✨ Key Features:

Comprehensive Coverage: Includes multiple countries with key metrics. Multiple Domains: Economic, social, environmental, technological, and military data. Up-to-date Information: Covers data from the last decade to provide recent insights. Research Ready: Suitable for academic research, visualizations, and analysis.

Electronic Health Legal Data

Exploring Laws and Regulations

By US Open Data Portal, data.gov [source]

About this dataset

This Electronic Health Information Legal Epidemiology dataset offers an extensive collection of legal and epidemiological data that can be used to understand the complexities of electronic health information. It contains a detailed balance of variables, including legal requirements, enforcement mechanisms, proprietary tools, access restrictions, privacy and security implications, data rights and responsibilities, user accounts and authentication systems. This powerful set provides researchers with real-world insights into the functioning of EHI law in order to assess its impact on patient safety and public health outcomes. With such data it is possible to gain a better understanding of current policies regarding the regulation of electronic health information as well as their potential for improvement in safeguarding patient confidentiality. Use this dataset to explore how these laws impact our healthcare system by exploring patterns across different groups over time or analyze changes leading up to new versions or updates. Make exciting discoveries with this comprehensive dataset!

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How to use the dataset

• Start by familiarizing yourself with the different columns of the dataset. Examine each column closely and look up any unfamiliar terminology to get a better understanding of what the columns are referencing.

• Once you understand the data and what it is intended to represent, think about how you might want to use it in your analysis. You may want to create a research question, or narrower focus for your project surrounding legal epidemiology of electronic health information that can be answered with this data set.

• After creating your research plan, begin manipulating and cleaning up the data as needed in order to prepare it for analysis or visualization as specified in your project plan or research question/model design steps you have outlined .

4 .Next, perform exploratory data analysis (EDA) on relevant subsets of data from specific countries if needed on specific subsets based on targets of interests (e.g gender). Filter out irrelevant information necessary for drawing meaningful insights; analyze patterns and trends observed in your filtered datasets ; compare areas which have differing rates e-health related rules and regulations tying decisions made by elected officials strongly driven by demographics , socioeconomics factors ,ideology etc.. . Look out for correlations using statistical information as needed throughout all stages in process from filtering out dis-informative subgroups from full population set til generating visualizations(graphs/ diagrams) depicting valid insight leveraging descriptive / predictive models properly validate against reference datasets when available always keep openness principal during gathering info especially when needs requires contact external sources such validating multiple sources work best provide strong seals establishing validity accuracy facts statement representing humans case scenarios digital support suitably localized supporting local languages culture respectively while keeping secure datasets private visible limited particular users duly authorized access 5 Finally create concrete summaries reporting discoveries create share findings preferably infographics showcasing evidence observances providing overall assessment main conclusions protocols developed so far broader community indirectly related interested professionals able benefit those results ideas complete transparently freely adapted locally ported increase overall global society level enhancing potentiality range impact derive conditions allowing wider adoption increased usage diffusion capture wide spread change movement affect global e-health legal domain clear manner

Research Ideas

• Studying how technology affects public health policies and practice - Using the data, researchers can look at the various types of legal regulations related to electronic health information to examine any relations between technology and public health decisions in certain areas or regions.
• Evaluating trends in legal epidemiology – With this data, policymakers can identify patterns that help measure the evolution of electronic health information regulations over time and investigate why such rules are changing within different states or countries.
• Analysing possible impacts on healthcare costs – Looking at changes in laws, regulations, and standards relate...

Life expectancy vs. health expenditure, 2022

Health expenditure includes all financing schemes and covers all aspects of healthcare. This data is adjusted forinflation and differences in the cost of living between countries.

This dataset is tha data of the https://ourworldindata.org/grapher/life-expectancy-vs-health-expenditure

Global Health Outcomes Data

Impact on Mortality Rates and Malnutrition in Countries Around the World

By Humanitarian Data Exchange [source]

About this dataset

This dataset provides comprehensive insights into critical health conditions around the world, such as mortality rate, malnutrition levels, and frequency of preventable diseases. It documents the prevalence of life-threatening diseases like malaria and tuberculosis, and are tracked alongside key health indicators like adult mortality rates, HIV prevalence, physicians per 10,000 people ratio and public health expenditures. Such metrics provide us with an accurate picture of how developed healthcare systems are in certain countries which ultimately leads to improvements in public policy formation and awareness amongst decision-makers. With this data it is possible to observe disparities between different regions of the world which can help inform global strategies for providing equitable care globally. This dataset is a valuable source for researchers interested in understanding global health trends over time or seeking to evaluate regional differences within countries

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How to use the dataset

This dataset provides comprehensive global health outcome data for countries around the world. It includes vital information such as infant mortality rates, child malnutrition rates, adult mortality rates, deaths due to malaria and tuberculosis, HIV prevalence rates, life expectancy at age 60 and public health expenditure. This dataset can be used to gain valuable insight into the challenges faced by different countries in providing a good quality of life for their citizens.

To use this dataset, first identify what questions you need answered and what outcomes you are looking to measure. You may want to look at specific disease-based indicators (e.g. malaria or tuberculosis), health-related indicators (e.g., nutrition), or overall population markers (e.g., life expectancy).

Then decide which data points from the provided fields will help answer your questions and provide the results needed - e.g,. infant mortality rate or HIV prevalence rate - extracting these values from relevant columns like “Infants lacking immunization (% of one-year-olds) Measles 2013” or “HIV prevalence, adult (% ages 15Ð49) 2013” respectively

Next extract other columnwise relevant information - e.g., country name — that could also aid your analysis using tools like Excel or Python's Pandas library; sorting through them based on any metric desired — e..g,, physicians per 10k people — while being mindful that some data points are missing in some cases (denoted by NA).

Finally perform basic analyses with either your own scripting language, like R/Python libraries' numerical functions with accompanying visuals/graphs etc if elucidating trends is desired; drawing meaningful conclusions about overall state of global health outcomes accordingly before making informed decisions thereafter if needed too!

Research Ideas

• Create a world health map to visualize the differences in health outcomes across different countries and regions.
• Develop an AI-based decision support tool that identifies optimal public health policies or interventions based on these metrics for different countries.
• Design a dashboard or web app that displays and updates this data in real-time, to allow users to compare the current state of global health indicators and benchmark them against historical figures

Acknowledgements

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

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: health-outcomes-csv-1.csv | Column name | Description | |:-------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------| | Country | The name of the country. (String) ...

This statistic shows a ranking of the estimated current healthcare spending in 2020 in Asia, differentiated by country. The spending refers to current spending of both governments and consumers.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than *** countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).