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.

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.

Global Share of Population Spending More than 10% of Household Consumption or Income on Out-of-Pocket Healthcare Expenditure by Country, 2023 Discover more data with ReportLinker!

The current healthcare spending per capita in Tunisia was forecast to continuously increase between 2024 and 2029 by in total **** U.S. dollars (+***** percent). After the seventh consecutive increasing year, the spending is estimated to reach ****** U.S. dollars and therefore a new peak in 2029. Depicted here is the average per capita spending, in a given country or region, with regards to healthcare. The spending refers to the average current spending of both governments and consumers per inhabitant.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 up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the current healthcare spending per capita in countries like Morocco and Sudan.

Global Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure by Country, 2023 Discover more data with ReportLinker!

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)

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.

Global Domestic General Government Health Expenditure Per Capita by Country, 2023 Discover more data with ReportLinker!

The current healthcare spending per capita in Southern Asia was forecast to continuously increase between 2024 and 2029 by in total 34.9 U.S. dollars (+44.57 percent). After the eleventh consecutive increasing year, the spending is estimated to reach 113.24 U.S. dollars and therefore a new peak in 2029. Depicted here is the average per capita spending, in a given country or region, with regards to healthcare. The spending refers to the average current spending of both governments and consumers per inhabitant.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 up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the current healthcare spending per capita in countries like Central Asia and Southeast Asia.

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.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_a40c83a6f36893fc4611eda91f84eb6b/view

Context

Health care in the United States is provided by many distinct organizations. Health care facilities are largely owned and operated by private sector businesses. 58% of US community hospitals are non-profit, 21% are government owned, and 21% are for-profit. According to the World Health Organization (WHO), the United States spent more on healthcare per capita ($9,403), and more on health care as percentage of its GDP (17.1%), than any other nation in 2014. Many different datasets are needed to portray different aspects of healthcare in US like disease prevalences, pharmaceuticals and drugs, Nutritional data of different food products available in US. Such data is collected by surveys (or otherwise) conducted by Centre of Disease Control and Prevention (CDC), Foods and Drugs Administration, Center of Medicare and Medicaid Services and Agency for Healthcare Research and Quality (AHRQ). These datasets can be used to properly review demographics and diseases, determining start ratings of healthcare providers, different drugs and their compositions as well as package informations for different diseases and for food quality. We often want such information and finding and scraping such data can be a huge hurdle. So, Here an attempt is made to make available all US healthcare data at one place to download from in csv files.

Content

• Nhanes Survey (National Health and Nutrition Examination Survey) - The National Health and Nutrition Examination Survey (NHANES) is a program of studies designed to assess the health and nutritional status of adults and children in the United States. The survey is unique in that it combines interviews and physical examinations. NHANES is a major program of the National Center for Health Statistics (NCHS). NCHS is part of the Centers for Disease Control and Prevention (CDC) and has the responsibility for producing vital and health statistics for the Nation. The NHANES interview includes demographic, socioeconomic, dietary, and health-related questions. The examination component consists of medical, dental, and physiological measurements, as well as laboratory tests administered by highly trained medical personnel. The diseases, medical conditions, and health indicators to be studied include: Anemia, Cardiovascular disease, Diabetes, Environmental exposures, Eye diseases, Hearing loss, Infectious diseases, Kidney disease, Nutrition, Obesity, Oral health, Osteoporosis, Physical fitness and physical functioning, Reproductive history and sexual behavior, Respiratory disease (asthma, chronic bronchitis, emphysema), Sexually transmitted diseases, Vision. 10000 individuals are surveyed to represent US statistics. Five files in this datasets represent current recent Nhanes data -
  Nhanes_2005_2006.csv
  Nhanes_2007_2008.csv
  Nhanes_2009_2010.csv
  Nhanes_2011_2012.csv
  Nhanes_2013_2014.csv
  
  • Data fields' description -
    • Nhanes_2005_2006.csv - Demographic, Dietary, Examinations, Laboratory
    • Nhanes_2007_2008.csv - Demographic, Dietary, Examinations, Laboratory
    • Nhanes_2009_2010.csv - Demographic, Dietary, Examinations, Laboratory
    • Nhanes_2011_2012.csv - Demographic, Dietary, [Examinations](http://https://wwwn.cdc.gov/nchs/nhanes/search/variab...

Medicare Spending per Beneficiary

Detailed Hospital Expense Breakdown

By Health [source]

About this dataset

This file allows healthcare executives and analysts to make informed decisions regarding how well continued improvements are being made over time so that they can understand how efficient they are fulfilling treatments while staying within budgetary constraints. Additionally, it’ll also help them map out trends amongst different hospitals and spot anomalies that could indicate areas where decisions should be reassessed as needed

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How to use the dataset

This dataset can provide valuable insights into how Medicare is spending per patient at specific hospitals in the United States. It can be used to gain a better understanding of the types of services covered under Medicare, and to what extent those services are being used. By comparing the average Medicare spending across different hospitals, users can also gain insight into potential disparities in care delivery or availability.

To use this dataset, first identify which hospital you are interested in analyzing. Then locate the row for that hospital in the dataset and review its associated values: value, footnote (optional), and start/end dates (optional). The Value column refers to how much Medicare spends on each particular patient; this is a numerical value represented as a decimal number up to 6 decimal places. The Footnote (optional) provides more information about any special circumstances that may need attention when interpreting the value data points. Finally, if Start Date and End Date fields are present they will specify over what timeframe these values were aggregated over.

Once all relevant data elements have been reviewed successively for all hospitals of interest then comparison analysis among them can be conducted based on Value, Footnote or Start/End dates as necessary to answer specific research questions or formulate conclusions about how Medicare is spending per patient at various hospitals nationwide

Research Ideas

• Developing a cost comparison tool for hospitals that allows patients to compare how much Medicare spends per patient across different hospitals.
• Creating an algorithm to help predict Medicare spending at different facilities over time and build strategies on how best to manage those costs.
• Identifying areas in which a hospital can save money by reducing unnecessary spending in order to reduce overall Medicare expenses

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: Medicare_hospital_spending_per_patient_Medicare_Spending_per_Beneficiary_Additional_Decimal_Places.csv | Column name | Description | |:---------------|:--------------------------------------------------------------------------------------| | Value | The amount of Medicare spending per patient for a given hospital or region. (Numeric) | | Footnote | Any additional notes or information related to the value. (Text) | | Start_Date | The start date of the period for which the value applies. (Date) | | End_Date | The end date of the period for which the value applies. (Date) |

Acknowledgements

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

European Healthcare Expenditure on Laboratory Services by Country, 2023 Discover more data with ReportLinker!

This dataset was utilized a join from enriched tables from ESRI which was curated from the 2020 Census from the United States Census Bureau, American Community Survey (ACS) and for county boundaries created by Office of Information Technology Services Next Generation 9-1-1 team in collaboration with all 44 counties of Idaho. This layer has information for all cities within Idaho regarding the county population health care consumer spending for 2024.For more information on how the data is curated for the Enrich tool please go the link below. 2024/2029 Esri Updated Demographics

Summary The City and County of San Francisco contracts with hundreds of nonprofit organizations to provide services for San Franciscans. These services include healthcare, legal aid, shelter, children’s programming, and more. This dataset contains all payments issued to nonprofit organizations by City departments since FY2019. This dataset will be updated at the close of each fiscal year. The underlying data is pulled from Supplier Payments on SF OpenBook. Please use SF OpenBook to find current-year data. The data in this dataset are presented in easy-to-read dashboards on our website. View the dashboards here: https://www.sf.gov/data/san-francisco-nonprofit-contracts-and-spending. How the dataset is created The Controller’s Office performs several significant data cleaning steps before uploading this dataset to the SF Open Data Portal. Please read the cleaning steps below: Cleaning Steps 1. SF OpenBook provides a filter labeled “Non-Profits Only” (Yes, No), and resulting datasets exported from SF OpenBook include a “Non Profit” column to indicate whether the supplier is a nonprofit (Yes, Blank). However, this field is not always accurate and excludes about 150 known nonprofits that are not labeled as a nonprofit in the City’s financial system. To ensure a complete dataset, we exported a full list of supplier payment data from SF OpenBook with the “Non-Profits Only” field filtered to “No” which provides a list of all supplier payments regardless of nonprofit status. Payments to suppliers may include payments for services to the City, such as memberships, use of space, and passthrough payments to beneficiaries. To identify payments specifically for contracted services delivered by nonprofits, we also filtered the SF Open Book report to only include the following types of goods and services prior to exporting the data: Community Based Org Svcs; Homeless Hsng And Supportv Service; Profess & Specialized Svcs; Professional/Specialized Svcs. We cleaned this data by adding a new “Nonprofit” column within the dataset and used this column to note a nonprofit status of “CON-marked Nonprofit” for approximately 150 known nonprofit suppliers without this indicator flagged in the financial system in addition to any nonprofits already accurately flagged in the system. We then filtered the full dataset using the new nonprofit column and used the filtered data for all of the dashboards on the webpage linked above. The list of excluded nonprofits may change over time as information gets updated in the City’s data system. Download the cleaned and updated dataset on the City’s Open Data Portal, which includes all of the known nonprofits. While the University of California, San Francisco (UCSF) is technically not-for-profit, a university’s financial management is very different from traditional nonprofit service providers, and the City’s agreement with UCSF includes hospital staffing in addition to contracted services to the public. The Controller's Office used the "Nonprofit" column to be able to exclude payments to UCSF when reporting on overall spending. There are divisions of UCSF that provide more traditional contracted services, but these cannot be clearly identified in the data. Note that filtering out this data may reflect an underrepresentation of overall spending. The Controller's Office also excludes several specific contracts that are predominately “pass through” payments where the nonprofit provider receives funds that they disperse to other agencies, such as for childcare or workforce subsidies. These types of contracts are substantially different from contracts where the nonprofit is providing direct services to San Franciscans. As of reporting for FY25 data, this dataset excluded the following Contract numbers related to pass-through payments for childcare subsidies: 1000026979, 1000028798, 1000027035, 1000008034. Update process This dataset will be manually updated

The current healthcare spending per capita in Russia was forecast to continuously increase between 2024 and 2029 by in total ***** U.S. dollars (+***** percent). After the sixth consecutive increasing year, the spending is estimated to reach ******* U.S. dollars and therefore a new peak in 2029. Depicted here is the average per capita spending, in a given country or region, with regards to healthcare. The spending refers to the average current spending of both governments and consumers per inhabitant.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 up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the current healthcare spending per capita in countries like Central & Western Europe and Eastern Europe.

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

The study’s objective was to explore the association between the components of fixed and variable Minimum Basic Care (Portuguese: PAB), sociodemographic factors, epidemiological profile, and municipal spending in primary health care in Rio Grande do Sul State, Brazil. An ecological study in 496 municipalities (counties) in the state was carried out. Mean variable municipal spending from 2011 to 2013 from the financial block of primary health care, representing the actual spending with federal budget transfers, was based on data from the Management Report of the Strategic Management Support Room, and multiple linear regression was used. To adjust the model, variables were grouped in five blocks according to the study’s objective. Mean spending on primary health care was BRL 81.20 (SD ± 35.50) per inhabitant-year. The block of variables comprising the fixed PAB component explained 39% (R2 = 0.39) of the variability in spending between municipalities, while for the variable PAB block, R2 was 0.82, in the sociodemographic block, R2 was 0.26, in the structure-performance block R2 was 0.46, and in the epidemiological profile block the R2 was 0.15. In the final model, the variable associated with the highest estimated values for spending on primary health care was the rate of family health teams. Municipalities with 135 to 41 teams per 100,000 inhabitant-years spend BRL 51.00 more per capita than municipalities with zero to 0 to 8 teams. Spending on primary health care appears to be linked more to federal induction than to factors associated with health care demand, such as the demographic and epidemiological profile of the municipalities in the state of Rio Grande do Sul.