Among OECD member countries, the United States had the highest percentage of gross domestic product spent on health care as of 2023. The U.S. spent nearly ** percent of its GDP on health care services. Germany, France and Japan followed the U.S. with distinctly smaller percentages. The United States had both significantly higher private and public spending on health compared with other developed countries. Why compare OECD countries?OECD stands for Organization for Economic Co-operation and Development. It is an economic organization consisting of ** members, mostly high-income countries and committed to democratic principles and market economy. This makes OECD statistics more comparable than statistics of developed and undeveloped countries. Health economics is an important matter for the OECD, even more since increasing health costs and an aging population have become an issue for many developed countries. Health costs in the U.S.  A higher GDP share spent on health care does not automatically lead to a better functioning health system. In the case of the U.S., high spending is mainly because of higher costs and prices, not due to higher utilization. For example, physicians’ salaries are much higher in the U.S. than in other comparable countries. A doctor in the U.S. earns almost twice as much as the average physician in Germany. Pharmaceutical spending per capita is also distinctly higher in the United States. Furthermore, the U.S. also spends more on health administrative costs compare to other wealthy countries.

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.

United States US: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data was reported at 0.781 % in 2013. This records a decrease from the previous number of 0.856 % for 2012. United States US: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data is updated yearly, averaging 0.880 % from Dec 1995 (Median) to 2013, with 18 observations. The data reached an all-time high of 1.078 % in 2000 and a record low of 0.724 % in 2008. United States US: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Poverty. Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure, expressed as a percentage of a total population of a country; ; Wagstaff et al. Progress on catastrophic health spending: results for 133 countries. A retrospective observational study, Lancet Global Health 2017.; Weighted Average;

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!

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 global current healthcare spending per capita in was forecast to continuously increase between 2024 and 2029 by in total 385.2 U.S. dollars (+26.13 percent). After the fourteenth consecutive increasing year, the spending is estimated to reach 1,859.33 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 Africa and North America.

The current healthcare spending in Europe was forecast to continuously increase between 2024 and 2029 by in total 843.8 billion U.S. dollars (+28.57 percent). After the seventh consecutive increasing year, the spending is estimated to reach 3.8 trillion U.S. dollars and therefore a new peak in 2029. According to Worldbank health spending includes expenditures with regards to healthcare services and goods. 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 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 in countries like Worldwide and Caribbean.

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.

Analysis of ‘World Bank WDI 2.12 - Health Systems’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/danevans/world-bank-wdi-212-health-systems on 21 November 2021.

--- Dataset description provided by original source is as follows ---

World Bank - World Development Indicators: Health Systems

This is a digest of the information described at http://wdi.worldbank.org/table/2.12# It describes various health spending per capita by Country, as well as doctors, nurses and midwives, and specialist surgical staff per capita

Content

Notes, explanations, etc. 1. There are countries/regions in the World Bank data not in the Covid-19 data, and countries/regions in the Covid-19 data with no World Bank data. This is unavoidable. 2. There were political decisions made in both datasets that may cause problems. I chose to go forward with the data as presented, and did not attempt to modify the decisions made by the dataset creators (e.g., the names of countries, what is and is not a country, etc.).

Columns are as follows: 1. Country_Region: the region as used in Kaggle Covid-19 spread data challenges. 2. Province_State: the region as used in Kaggle Covid-19 spread data challenges. 3. World_Bank_Name: the name of the country used by the World Bank 4. Health_exp_pct_GDP_2016: Level of current health expenditure expressed as a percentage of GDP. Estimates of current health expenditures include healthcare goods and services consumed during each year. This indicator does not include capital health expenditures such as buildings, machinery, IT and stocks of vaccines for emergency or outbreaks.

1. Health_exp_public_pct_2016: Share of current health expenditures funded from domestic public sources for health. Domestic public sources include domestic revenue as internal transfers and grants, transfers, subsidies to voluntary health insurance beneficiaries, non-profit institutions serving households (NPISH) or enterprise financing schemes as well as compulsory prepayment and social health insurance contributions. They do not include external resources spent by governments on health.

2. Health_exp_out_of_pocket_pct_2016: Share of out-of-pocket payments of total current health expenditures. Out-of-pocket payments are spending on health directly out-of-pocket by households.

3. Health_exp_per_capita_USD_2016: Current expenditures on health per capita in current US dollars. Estimates of current health expenditures include healthcare goods and services consumed during each year.

4. per_capita_exp_PPP_2016: Current expenditures on health per capita expressed in international dollars at purchasing power parity (PPP).

5. External_health_exp_pct_2016: Share of current health expenditures funded from external sources. External sources compose of direct foreign transfers and foreign transfers distributed by government encompassing all financial inflows into the national health system from outside the country. External sources either flow through the government scheme or are channeled through non-governmental organizations or other schemes.

6. Physicians_per_1000_2009-18: Physicians include generalist and specialist medical practitioners.

7. Nurse_midwife_per_1000_2009-18: Nurses and midwives include professional nurses, professional midwives, auxiliary nurses, auxiliary midwives, enrolled nurses, enrolled midwives and other associated personnel, such as dental nurses and primary care nurses.

8. Specialist_surgical_per_1000_2008-18: Specialist surgical workforce is the number of specialist surgical, anaesthetic, and obstetric (SAO) providers who are working in each country per 100,000 population.

9. Completeness_of_birth_reg_2009-18: Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered.

10. Completeness_of_death_reg_2008-16: Completeness of death registration is the estimated percentage of deaths that are registered with their cause of death information in the vital registration system of a country.

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Inspiration

Does health spending levels (public or private), or hospital staff have any effect on the rate at which Covid-19 spreads in a country? Can we use this data to predict the rate at which Cases or Fatalities will grow?

--- Original source retains full ownership of the source dataset ---

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.

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

The current healthcare spending in Southeast Asia was forecast to continuously increase between 2024 and 2029 by in total 98.6 billion U.S. dollars (+52.88 percent). After the fifteenth consecutive increasing year, the spending is estimated to reach 285 billion U.S. dollars and therefore a new peak in 2029. Notably, the current healthcare spending of was continuously increasing over the past years.According to Worldbank health spending includes expenditures with regards to healthcare services and goods. 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 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 in countries like Central Asia and Southern Asia.

The U.S. Census Bureau, in collaboration with five federal agencies, launched the Household Pulse Survey to produce data on the social and economic impacts of Covid-19 on American households. The Household Pulse Survey was designed to gauge the impact of the pandemic on employment status, consumer spending, food security, housing, education disruptions, and dimensions of physical and mental wellness. The survey was designed to meet the goal of accurate and timely weekly estimates. It was conducted by an internet questionnaire, with invitations to participate sent by email and text message. The sample frame is the Census Bureau Master Address File Data. Housing units linked to one or more email addresses or cell phone numbers were randomly selected to participate, and one respondent from each housing unit was selected to respond for him or herself. Estimates are weighted to adjust for nonresponse and to match Census Bureau estimates of the population by age, gender, race and ethnicity, and educational attainment. All estimates shown meet the NCHS Data Presentation Standards for Proportions.

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. We cleaned this data by adding a new “Nonprofit” column within the dataset and used this column to note a nonprofit status of “Yes” 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. 2. 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. As such, the Controller's Office created a 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. 3. 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. Update process This dataset will be manually updated after year-end financial processing is complete, typically in September. There may be a delay between the end of the fiscal year and the publication of this dataset.

Analysis of ‘Socio-Economic Country Profiles’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/nishanthsalian/socioeconomic-country-profiles on 28 January 2022.

--- Dataset description provided by original source is as follows ---

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

--- Original source retains full ownership of the source dataset ---

Canada CA: Number of People Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure data was reported at 175,000.000 Person in 2010. This records an increase from the previous number of 127,000.000 Person for 2007. Canada CA: Number of People Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure data is updated yearly, averaging 110,000.000 Person from Dec 1994 (Median) to 2010, with 5 observations. The data reached an all-time high of 175,000.000 Person in 2010 and a record low of 80,000.000 Person in 1994. Canada CA: Number of People Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank.WDI: Social: Poverty and Inequality. Number of people spending more than 25% of household consumption or income on out-of-pocket health care expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).;Global Health Observatory. Geneva: World Health Organization; 2023. (https://www.who.int/data/gho/data/themes/topics/financial-protection);Sum;This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/].

Forecast: Share of Population Spending More than 10% of Household Consumption or Income on Out-of-Pocket Healthcare Expenditure in Japan 2024 - 2028 Discover more data with ReportLinker!

The current healthcare spending per capita in India was forecast to continuously increase between 2024 and 2029 by in total 40.8 U.S. dollars (+47.71 percent). After the eleventh consecutive increasing year, the spending is estimated to reach 126.27 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 Pakistan and Sri Lanka.

U.S. Household Mental Health & Covid-19

Assessing the Impact of the Pandemic

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

About this dataset

This dataset offers a closer look into the mental health care received by U.S. households in the last four weeks during the Covid-19 pandemic. The sheer scale of this crisis is inspiring people of all ages, backgrounds, and geographies to come together to tackle the problem. The Household Pulse Survey from the U.S. Census Bureau was published with federal agency collaboration in order to draw up accurate and timely estimates about how Covid-19 is impacting employment status, consumer spending, food security, housing stability, education interruption, and physical and mental wellness amongst American households. In order to deliver meaningful results from this survey data about wellbeing at various levels of society during this trying period – which includes demographic characteristics such as age gender race/ethnicity training attainment – each consulted household was randomly selected according to certain weighted criteria to maintain accuracy throughout the findings This dataset will help you explore what's it like on the ground right now for everyone affected by Covid-19 - Will it inform your decisions or point you towards new opportunities?

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How to use the dataset

This dataset contains information about the mental health care that U.S. households have received in the last 4 weeks, during the Covid-19 pandemic. This data is valuable when wanting to track and measure mental health needs across the country and draw comparisons between regions based on support available.

To use this dataset, it is important to understand each of its columns or variables in order to draw meaningful insights from the data. The ‘Indicator’ column indicates which type of indicator (percentage or absolute number) is being measured by this survey, while ‘Group’ and 'Subgroup' provide more specific details about who was surveyed for each indicator included in this dataset.

The Columns ‘Phase’ and 'Time Period' provide information regarding when each of these indicators was measured - whether during a certain phase or over a particular timespan - while columns such as 'Value', 'LowCI' & 'HighCI' show us how many individuals fell into what quartile range for each measurement taken (e.g., how many people reported they rarely felt lonely). Similarly, the column Suppression Flag helps us identify cases where value has been suppressed if it falls below a certain benchmark; this allows us to calculate accurate estimates more quickly without needing to sort through all suppressed values manually each time we use this dataset for analysis purposes. Finally, columns such as ‘Time Period Start Date’ & ‘Time Period End Date’ indicate which exact dates were used for measurements taken over different periods throughout those dates specified – useful when conducting time-series related analyses over longer periods of time within our research scope)

Overall, when using this dataset it's important to keep in mind exactly what indicator type you're looking at - percentage points or absolute numbers - as well its associated group/subgroup characteristics so that you can accurately interpret trends based on key findings had by interpreting any correlations drawn from these results!

Research Ideas

• Analyzing the effects of the Covid-19 pandemic on mental health care among different subgroups such as racial and ethnic minorities, gender and age categories.
• Identifying geographical disparities in mental health services by comparing state level data for the same time period.
• Comparing changes in mental health care indicators over time to understand how the pandemic has impacted people's access to care within a quarter or over longer periods

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. ...

Colombia CO: Health Expenditure: Public: % of GDP data was reported at 5.410 % in 2014. This records an increase from the previous number of 5.219 % for 2013. Colombia CO: Health Expenditure: Public: % of GDP data is updated yearly, averaging 4.937 % from Dec 1995 (Median) to 2014, with 20 observations. The data reached an all-time high of 6.459 % in 1999 and a record low of 3.721 % in 1995. Colombia CO: Health Expenditure: Public: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Colombia – Table CO.World Bank.WDI: Social: Health Statistics. Public health expenditure consists of recurrent and capital spending from government (central and local) budgets, external borrowings and grants (including donations from international agencies and nongovernmental organizations), and social (or compulsory) health insurance funds.; ; World Health Organization Global Health Expenditure database (see http://apps.who.int/nha/database for the most recent updates).; Weighted average;