United States US: Health Expenditure: Public: % of GDP data was reported at 8.279 % in 2014. This records an increase from the previous number of 8.045 % for 2013. United States US: Health Expenditure: Public: % of GDP data is updated yearly, averaging 6.710 % from Dec 1995 (Median) to 2014, with 20 observations. The data reached an all-time high of 8.279 % in 2014 and a record low of 5.614 % in 1999. United States US: 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 USA – Table US.World Bank: 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;

United States US: Health Expenditure: Total: % of GDP data was reported at 17.141 % in 2014. This records an increase from the previous number of 16.898 % for 2013. United States US: Health Expenditure: Total: % of GDP data is updated yearly, averaging 15.145 % from Dec 1995 (Median) to 2014, with 20 observations. The data reached an all-time high of 17.141 % in 2014 and a record low of 12.963 % in 1997. United States US: Health Expenditure: Total: % of GDP 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: Health Statistics. Total health expenditure is the sum of public and private health expenditure. It covers the provision of health services (preventive and curative), family planning activities, nutrition activities, and emergency aid designated for health but does not include provision of water and sanitation.; ; World Health Organization Global Health Expenditure database (see http://apps.who.int/nha/database for the most recent updates).; Weighted average;

Context

This indicator calculates the average expenditure on health per person. It contributes to understand the health expenditure relative to the population size facilitating international comparison. The Organization for Economic Co-operation and Development (OECD) defines current health spending as:

Health spending measures the final consumption of health care goods and services (i.e. current health expenditure) including personal health care (curative care, rehabilitative care, long-term care, ancillary services and medical goods) and collective services (prevention and public health services as well as health administration), but excluding spending on investments. Health care is financed through a mix of financing arrangements including government spending and compulsory health insurance (“Government/compulsory”) as well as voluntary health insurance and private funds such as households’ out-of-pocket payments, NGOs and private corporations (“Voluntary”). This indicator is presented as a total and by type of financing (“Government/compulsory”, “Voluntary”, “Out-of-pocket”) and is measured as a share of GDP, as a share of total health spending and in USD per capita (using economy-wide PPPs).

OECD (2020), Health spending (indicator). doi: 10.1787/8643de7e-en (Accessed on 19 September 2020)

This statistic shows a ranking of the estimated current health expenditure share of GDP in 2020 in Latin America and the Caribbean, differentiated by country. The ratio refers to the share of total gross domestic product (GDP).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 150 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).

The data set records the health expenditure of 2000-2015 countries along 65 countries along the belt and road. Current expenditures on health per capita in current US dollars. Estimates of current health expenditures include healthcare goods and services consumed during each year.Data sources: World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database). The data set contains 2 tables：Current health expenditure (% of GDP),Current health expenditure (% of GDP)

Explore the World Competitiveness Ranking dataset for 2016, including key indicators such as GDP per capita, fixed telephone tariffs, and pension funding. Discover insights on social cohesion, scientific research, and digital transformation in various countries.

Social cohesion, The image abroad of your country encourages business development, Scientific articles published by origin of author, International Telecommunication Union, World Telecommunication/ICT Indicators database, Data reproduced with the kind permission of ITU, National sources, Fixed telephone tariffs, GDP (PPP) per capita, Overall, Exports of goods - growth, Pension funding is adequately addressed for the future, Companies are very good at using big data and analytics to support decision-making, Gross fixed capital formation - real growth, Economic Performance, Scientific research legislation, Percentage of GDP, Health infrastructure meets the needs of society, Estimates based on preliminary data for the most recent year., Singapore: including re-exports., Value, Laws relating to scientific research do encourage innovation, % of GDP, Gross Domestic Product (GDP), Health Infrastructure, Digital transformation in companies is generally well understood, Industrial disputes, EE, Female / male ratio, State ownership of enterprises, Total expenditure on R&D (%), Score, Colombia, Estimates for the most recent year., Percentage change, based on US$ values, Number of listed domestic companies, Tax evasion is not a threat to your economy, Scientific articles, Tax evasion, % change, Use of big data and analytics, National sources, Disposable Income, Equal opportunity, Listed domestic companies, Government budget surplus/deficit (%), Pension funding, US$ per capita at purchasing power parity, Estimates; US$ per capita at purchasing power parity, Image abroad or branding, Equal opportunity legislation in your economy encourages economic development, Number, Article counts are from a selection of journals, books, and conference proceedings in S&E from Scopus. Articles are classified by their year of publication and are assigned to a region/country/economy on the basis of the institutional address(es) listed in the article. Articles are credited on a fractional-count basis. The sum of the countries/economies may not add to the world total because of rounding. Some publications have incomplete address information for coauthored publications in the Scopus database. The unassigned category count is the sum of fractional counts for publications that cannot be assigned to a country or economy. Hong Kong: research output items by the higher education institutions funded by the University Grants Committee only., State ownership of enterprises is not a threat to business activities, Protectionism does not impair the conduct of your business, Digital transformation in companies, Total final energy consumption per capita, Social cohesion is high, Rank, MTOE per capita, Percentage change, based on constant prices, US$ billions, National sources, World Trade Organization Statistics database, Rank, Score, Value, World Rankings

Argentina, Australia, Austria, Belgium, Brazil, Bulgaria, Canada, Chile, China, Colombia, Croatia, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Kazakhstan, Latvia, Lithuania, Luxembourg, Malaysia, Mexico, Mongolia, Netherlands, New Zealand, Norway, Oman, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Saudi Arabia, Singapore, Slovenia, South Africa, Spain, Sweden, Switzerland, Thailand, Turkey, Ukraine, United Kingdom, Venezuela

Follow data.kapsarc.org for timely data to advance energy economics research.

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?

The table includes all raw data used for the calculation and high and low estimates. (XLSX)

Context

I wanted to see if there is some correlation with covid-19 mortality and other parameters

Content

The data is collected from the World Bank data sets. These features were collected: 'Gross domestic product 2019 (millions of US dollars)' 'Mobile cellular subscriptions (per 100 people)' 'Immunization, HepB3 (% of one-year-old children)' 'Immunization, Hib3 (% of children ages 12-23 months)' 'Immunization, BCG (% of one-year-old children)' 'Immunization, DPT (% of children ages 12-23 months)' 'Immunization, measles (% of children ages 12-23 months)' 'Immunization, Pol3 (% of one-year-old children)' 'Community health workers (per 1,000 people)' 'Nurses and midwives (per 1,000 people)' 'Physicians (per 1,000 people)' 'Incidence of malaria (per 1,000 population at risk)' 'Smoking prevalence, total, ages 15+' 'Number of surgical procedures (per 100,000 population)' 'People with basic handwashing facilities including soap and water (% of population)' 'Incidence of tuberculosis (per 100,000 people)' 'Increase in poverty gap at $1.90 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (USD)' 'Increase in poverty gap at $1.90 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (% of poverty line)' 'Increase in poverty gap at $3.20 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (USD)' 'Increase in poverty gap at $3.20 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (% of poverty line)' 'External health expenditure per capita (current US$)' 'Domestic general government health expenditure (% of GDP)' 'Domestic general government health expenditure (% of general government expenditure)' 'Domestic general government health expenditure per capita (current US$)' 'Domestic private health expenditure (% of current health expenditure)' 'Domestic private health expenditure per capita (current US$)' 'International migrant stock (% of population)' 'Number of people who are undernourished' 'Life expectancy at birth, total (years)' 'Population ages 65 and above, total' 'Population, total' 'Surface area (sq km)' 'Urban population (% of total population)' 'Adequacy of social insurance programs (% of total welfare of beneficiary households)']

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Is it possible to find more explanations on the sometimes strange differences between different countries regarding covid-19 infections and death cases

Average ratio of new chemical entities (NCE) registration fees to billions of US dollars in gross domestic product (GDP) and total health expenditure, by region, with and without population weights.

This dataset provides a country–year panel for OECD countries covering the period 2010–2024. It combines annual data on public, private and total social expenditure as a share of GDP with the World Happiness Index (WHI) and the Human Development Index (HDI).The data are constructed to analyze the relationships between social spending, subjective well-being and human development in OECD countries. The panel structure (one observation per country per year) makes the dataset suitable for descriptive analysis as well as regression-based empirical research.ContentsThe main Excel file contains a single data sheet:Sheet: data_setEach row corresponds to a specific country–year observation for an OECD member state.Variables:Country: Country name (OECD member; e.g., “Australia”, “Türkiye”, “United States”).iso3: ISO 3166-1 alpha-3 country code (e.g., “AUS”, “TUR”, “USA”).year: Calendar year (2010–2024).pub_socexp_gdp: Public social expenditure as a percentage of GDP (%).priv_socexp_gdp: Private (mandatory and voluntary) social expenditure as a percentage of GDP (%).tot_socexp_gdp: Total social expenditure (public + private) as a percentage of GDP (%).WHI: World Happiness Index; average national happiness score on a 0–10 scale based on the Cantril ladder question.HDI: Human Development Index; composite index of three basic dimensions of human development (health, education, and standard of living).income_group: Binary country income group indicator used in the analysis. High‑income OECD countries are coded as 1 (“High”), and all other OECD members (upper‑middle, lower‑middle and low income) are coded as 0 (“NonHigh”). Income groups were constructed using data from the OECD Data Explorer (2024) and the World Bank country income classification for 2024, based on PPP (purchasing power parity) income thresholds.Empty cells indicate that data for the corresponding country–year observation are not available in the original sources or were not included in the analytical sample due to missingness.Data sourcesSocial expenditure (pub_socexp_gdp, priv_socexp_gdp, tot_socexp_gdp)Data are taken from the OECD Social Expenditure Database (SOCX). SOCX provides reliable and internationally comparable statistics on public and mandatory and voluntary private social expenditure at the program level for 38 OECD countries (and some accession countries), with coverage from 1980 and estimates for more recent years.Reference: OECD Social Expenditure Database (SOCX), https://www.oecd.org/en/data/datasets/social-expenditure-database-socx.html.World Happiness Index (WHI)Happiness data are drawn from the World Happiness Report, accessed via HumanProgress.org (World Happiness Report section). The index is based on average national values for answers to the Cantril ladder question, which asks respondents to evaluate their current life on a 0–10 scale, with the worst possible life as 0 and the best possible life as 10.Reference: World Happiness Report; HumanProgress.org, https://humanprogress.org.Human Development Index (HDI)HDI data are drawn from the Human Development Index series compiled by the United Nations Development Programme (UNDP), accessed via HumanProgress.org (Human Development Index section). The HDI measures three basic dimensions of human development: life expectancy at birth; an education component (adult literacy rate and school enrollment); and GDP per capita (purchasing power parity, PPP, in U.S. dollars), combined into a composite index.Reference: United Nations Development Programme (UNDP), Human Development Reports; HumanProgress.org, https://humanprogress.org.Data construction and coverageThe dataset is restricted to OECD member countries and the years 2010–2024.WHI and HDI series are matched to OECD social expenditure data using ISO3 country codes and calendar years.In addition, a binary income group variable (income_group) was created to distinguish high‑income OECD countries from other OECD members, using the World Bank’s 2024 income thresholds (PPP‑based) and country information from the OECD Data Explorer (2024).Some country–year combinations, particularly in later years (e.g., 2022–2024), contain missing values where the original sources do not provide data or only provide partial estimates. These are retained as empty cells.The empirical analyses in the associated study are conducted on subsets of the data restricted to complete cases for the relevant variables.Researchers can use this dataset to replicate the results of the associated study or to conduct additional analyses on the links between social expenditure, happiness and human development within the OECD context.If you use this dataset, please cite both this data file and the original data providers (OECD, World Happiness Report, UNDP, and HumanProgress.org).

Don't forget to upvote when find this useful

Unveiling the Health and Demographics Dataset: Illuminating Life Expectancy

Description: Step into the world of global health and demographics with our rich and comprehensive dataset. It's your passport to unraveling the secrets of life expectancy and understanding the pulse of population health. Dive into a treasure trove of valuable information for public health research and epidemiology, where each column tells a unique story about a nation's health journey.

Discover the Gems in Our Dataset:

• Country: Explore the global tapestry with data from diverse nations.
• Year: Unlock the passage of time and its impact on health trends.
• Status: Understand the development status, whether "Developed" or "Developing," that shapes the course of health.
• Life Expectancy: Peer into the crystal ball of population health, revealing how long people can expect to live.
• Adult Mortality: Gauge the probabilities of survival between ages 15 and 60 per 1,000 population.
• Infant Deaths: Delve into infant health with the number of infant deaths per 1,000 live births.
• Alcohol: Raise a glass to insights on average alcohol consumption in liters per capita.
• Percentage Expenditure: Unearth health expenditure as a percentage of a country's GDP.
• Hepatitis B: Measure immunization coverage for Hepatitis B.
• Measles: Examine the impact of this preventable disease with the number of reported cases per 1,000 population.
• BMI: Step onto the scales of national health with the average Body Mass Index.
• Under-Five Deaths: Shine a spotlight on child mortality with the number of deaths under age five per 1,000 live births.
• Polio: Inspect immunization coverage for Polio.
• Total Expenditure: Track the total health expenditure as a percentage of GDP.
• Diphtheria: Assess immunization coverage for Diphtheria.
• HIV/AIDS: Witness the prevalence of HIV/AIDS as a percentage of the population.
• GDP: Follow the financial pulse of a nation with Gross Domestic Product data.
• Population: Witness the ebb and flow of a nation's populace.
• Thinness 1-19 Years: Explore the prevalence of thinness among children and adolescents aged 1-19.
• Thinness 5-9 Years: Zoom in on thinness among children aged 5-9.
• Income Composition of Resources: Decode the composite index reflecting income distribution and resource access.
• Schooling: Measure the gift of knowledge with data on average years of schooling.

Predictive Targets: - The "Life Expectancy" column is your North Star, guiding the way to predictive insights. Harness the power of data to predict life expectancy using the mosaic of health and demographic indicators at your disposal.

Journey with the Data: 1. Predicting Life Expectancy: Embark on the quest to build regression models that forecast life expectancy for diverse countries and years based on this wealth of features. 2. Identifying Influential Factors: Uncover the gems within the dataset that influence life expectancy the most, providing valuable insights for public health interventions. 3. Health Policy Analysis: Assess the impact of health expenditure, immunization coverage, and disease prevalence on life expectancy and shape policies that safeguard population health.

This dataset is your window into the intricate world of global health. Join us on a journey of discovery as we explore the factors shaping life expectancy and navigate the waters of public health, epidemiology, and population health.

Chapter 3 looks at the institutional factors that contribute to explaining the relationship between parent’s education and children’s education. Data for 48 countries in total, from multiple harmonized surveys, are utilised. A total of 149 surveys are included. Using multivariate regressions, we first present the correlation coefficients of the relationship between parent’s education and children’s education. These coefficients then serve as the dependent variable in the regression analysis with the institutional factors at the second stage. To this end, secondary data are obtained from the household Demographic and Health Surveys (DHS), and from the U.S. Agency for International Development (USAID) and the World Bank data catalogue. The DHS are nationally representative cross-sectional surveys where data on impact evaluation indicators on the population, health, and nutrition in over 90 countries are represented. The primary respondents of the surveys are women of reproductive age, between 15-49 years, who respond to a household questionnaire and a woman’s questionnaire (DHS Program, 2020). The man’s questionnaire is responded to by men of reproductive age (typically 15 to 49, 54, or 59). In the household questionnaire, the respondent provides information on household membership, individual characteristics, household head, health, housing, consumer goods, and living conditions (DHS Program, 2020). The factors from the USAID and the World Bank data catalogue are part of the world development indicators (WDI) and the worldwide governance indicators (WGI). Corruption estimates, political stability estimates, and voice and accountability estimates are taken from the WGI while the others (GDP, prevalence of HIV, life expectancy at birth, female-male labour force participation, government expenditure on education, pupil-teacher ratio, primary school starting age, primary school duration, secondary school duration, compulsory years of education, fixed telephone subscriptions, and mobile cellular subscriptions) are from the WDI. The WDI is a compilation of high-quality, relevant, and internationally comparable statistics about global development and the fight against poverty (World Bank, 2020b). 1600 time series indicators are contained in the database for 217 countries. These indicators are organized according to six main thematic areas that are poverty and inequality, people, environment, economy, states and markets, global links (World Bank, 2020b). The WGI are nationally comparable indicators of government selection, monitoring, replacement, effectiveness, and the respect of citizens and the state. The worldwide governance indicators generally report on six broad governance dimensions for over 215 countries and territories. These dimensions are government effectiveness, control of corruption, rule of law, voice and accountability, regulatory quality, and political stability and absence of violence (World Bank, 2019). Specifically, we focus on GDP, the prevalence of HIV, life expectancy at birth, female-male labour force participation, government expenditure on education, pupil-teacher ratio, primary school starting age, primary school duration, secondary school duration, compulsory years of education, fixed telephone subscriptions, mobile cellular subscriptions, the extent of corruption, the extent of political stability, and the extent of voice and accountability. The factors used in this chapter are selected based on data availability. The process looks at the correlation between these factors and the intergenerational correlation of education. The results show that these institutional factors account for 39% of the explained cross-country variation in the intergenerational correlation of education. The pupil-teacher ratio, primary school duration, and compulsory years of education reduce intergenerational correlation of education by 0.03 years, 0.03 years, and 0.02 years respectively, following a one standard deviation change in the variables. Besides these variables, GDP, female-male labour force participation, and extent of voice and accountability reduce intergenerational correlation of education by 0.01 years, 0.03 years, and 0.03 years respectively, following a one standard deviation change in the variables. This confirms our second hypothesis on favourable institutional characteristics being able to reduce intergenerational correlation of education.

🔗 Check out my notebook here: Link

This dataset includes malnutrition indicators and some of the features that might impact malnutrition. The detailed description of the dataset is given below:

• Percentage-of-underweight-children-data: Percentage of children aged 5 years or below who are underweight by country.

• Prevalence of Underweight among Female Adults (Age Standardized Estimate): Percentage of female adults whos BMI is less than 18.

• GDP per capita (constant 2015 US$): GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2015 U.S. dollars.

• Domestic general government health expenditure (% of GDP): Public expenditure on health from domestic sources as a share of the economy as measured by GDP.

• Maternal mortality ratio (modeled estimate, per 100,000 live births): Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP measured using purchasing power parities (PPPs).

• Mean-age-at-first-birth-of-women-aged-20-50-data: Average age at which women of age 20-50 years have their first child.

• School enrollment, secondary, female (% gross): Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers.

Top Covid19 Countries and Health Demographic Trend

Context

This is a time-series trend data collection with a series of json files primarily focused on countries most impacted by Covid-19. The tree formatted time series data should be able to enable various different kinds of analysis to answer questions about what may make a country's health system vulnerable to Covid-19 and what health demographics may help reducing the impact.

Demographic metrics

Healthcare GDP Expenditure 
Healthcare Employment
Hospital Bed Capacity
Air Pollution and Death Rate
Chronic illnesses and DALYs(Disability-Adjusted Life Years)
Body Weight 
Elderly(Aged 65+) Population
CT Scanner Density
Tobacco Consumption(Smoker population %)

More metrics can be added upon request.

Data Normalization

The raw CSV includes many different types of measurements such as number, percentage and per 1 million population. This data normalizes the time_series data by selecting data that is more about density, and number per capita data rather than absolute numbers. This could help doing comparison among nations since they may vary significantly on population.

Content

Most of the JSON files contain time_series data. For people who want to use the data as country metadata, the most-recent data attribute is collected in top_countries_latest_fact_summary.json

The JSON data focuses on the above mentioned demographic areas in a simple tree schema { Country_name: { metric_name:[ List of {year, value, unit} ] } }

Data source & License

The data is sourced from OECD(https://stats.oecd.org/) and GDHX(http://ghdx.healthdata.org/). The json files with prefix "gbd_" are from GDHX

Following citation is needed for using GDHX data:

GBD Results tool: Use the following to cite data included in this download: Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2017 (GBD 2017) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018. Available from http://ghdx.healthdata.org/gbd-results-tool.

Inspiration

• Where does US rank in term of Healthcare/Preventive spending in GDP, hospital bed/ICU bed/physician density and long-term illness? In which areas can US do more to prevent future Cov-19 crisis?

• Is there correlation in a nation's medical preparedness and the rate of growth in confirmation, death rate and recovery rate? From GBD data graphs, it seems that Dalys(DALYs (Disability-Adjusted Life Years), rate per 100k) can divided nations into different camps.

• How does death rate from Cov-19 correlate with Death rate related to Cardiovascular diseases and Chronic respiratory diseases?

• What trends can we discover in various nation's health demographics over time? Are some areas getting better while others getting worse?

• With time span from 2010 to 2018, this dataset can also correlate with data related to recent outbreaks such as seasonal flus, Avian influenza, etc.

Example Notebook

With some quick analysis, it shows that the US actually ranks higher than China for DALYs(Disability-adjusted life years) caused by Chronic Respiratory conditions, which could be due to seasonal allergies. It seems counter-intuitive that this may suggest that countries with cleaner air may have higher burden of people with Chronic Respiratory conditions that may have made them more vulnerable in the Covid-19 crisis.

Example Kernel: https://www.kaggle.com/timxia/bar-chart-comparison-of-countries https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4802460%2F2fce05195108856422b437316f34e837%2FTobacco.png?generation=1585936274243838&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4802460%2Fe8db14764a47a8bce48fa79bdfdfb0f1%2FChronicDisease.png?generation=1585936274372639&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4802460%2Fc534d40af042b9a503325f41c49b83cb%2FAirPollution.png?generation=1585936274337626&alt=media" alt="">