Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

Country: Name of the country.

Density (P/Km2): Population density measured in persons per square kilometer.

Abbreviation: Abbreviation or code representing the country.

Agricultural Land (%): Percentage of land area used for agricultural purposes.

Land Area (Km2): Total land area of the country in square kilometers.

Armed Forces Size: Size of the armed forces in the country.

Birth Rate: Number of births per 1,000 population per year.

Calling Code: International calling code for the country.

Capital/Major City: Name of the capital or major city.

CO2 Emissions: Carbon dioxide emissions in tons.

CPI: Consumer Price Index, a measure of inflation and purchasing power.

CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.

Currency_Code: Currency code used in the country.

Fertility Rate: Average number of children born to a woman during her lifetime.

Forested Area (%): Percentage of land area covered by forests.

Gasoline_Price: Price of gasoline per liter in local currency.

GDP: Gross Domestic Product, the total value of goods and services produced in the country.

Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.

Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.

Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.

Largest City: Name of the country's largest city.

Life Expectancy: Average number of years a newborn is expected to live.

Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.

Minimum Wage: Minimum wage level in local currency.

Official Language: Official language(s) spoken in the country.

Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.

Physicians per Thousand: Number of physicians per thousand people.

Population: Total population of the country.

Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.

Tax Revenue (%): Tax revenue as a percentage of GDP.

Total Tax Rate: Overall tax burden as a percentage of commercial profits.

Unemployment Rate: Percentage of the labor force that is unemployed.

Urban Population: Percentage of the population living in urban areas.

Latitude: Latitude coordinate of the country's location.

Longitude: Longitude coordinate of the country's location.

Potential Use Cases

Analyze population density and land area to study spatial distribution patterns.

Investigate the relationship between agricultural land and food security.

Examine carbon dioxide emissions and their impact on climate change.

Explore correlations between economic indicators such as GDP and various socio-economic factors.

Investigate educational enrollment rates and their implications for human capital development.

Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.

Study labor market dynamics through indicators such as labor force participation and unemployment rates.

Investigate the role of taxation and its impact on economic development.

Explore urbanization trends and their social and environmental consequences.

This statistic shows a ranking of the estimated average number of physicians per 1,000 inhabitants in 2020 in Latin America, differentiated by country.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).

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 ---

Series Name: Health worker density by type of occupation (per 10 000 population)Series Code: SH_MED_HEAWORRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 3.c.1: Health worker density and distributionTarget 3.c: Substantially increase health financing and the recruitment, development, training and retention of the health workforce in developing countries, especially in least developed countries and small island developing StatesGoal 3: Ensure healthy lives and promote well-being for all at all agesFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Forecast: Population Per Medical Doctors Graduates in Italy 2024 - 2028 Discover more data with ReportLinker!

Forecast: Population Per Medical Doctors Graduates in Germany 2024 - 2028 Discover more data with ReportLinker!

The policy objective of the Impact Evaluation (IE) is to build evidence on the impact and cost-effectiveness of the proposed Performance-Based-Financing (PBF) project in Tajikistan. More specifically, the IE would seek to ascertain: (i) the impact and cost-effectiveness of the PBF model implemented in Tajikistan; and (ii) whether PBF is more effective or cost-effective if implemented in conjunction with additional low cost interventions (Collaborative Quality Improvement, Citizen Report Cards). The results from the IE will help informing the MOH on whether PBF should be scaled-up to additional PHC level institutions in other regions. The Collaborative Quality Improvement intervention responds to policy concerns that performance incentives may not produce the desired improvements if providers lack the necessary competencies, data to inform decisions and knowledge. The Citizen Report Card attempts to improve the effectiveness of PBF by strengthening the 'short route of accountability', i.e., by increasing accountability of health facilities to their local constituents. Since PBF, collaborative quality improvement (CQI), and citizen report cards (CRC) have never been implemented in large scale in Tajikistan, it is to be expected that the results from the IE will be useful for designing national PHC policy in Tajikistan, and that they will also contribute to the larger body of knowledge on these interventions. The IE employs both difference-in-difference and experimental approaches to identify the impact of the different combinations of interventions. Assignment to PBF was not random. Three districts in the Sughd region and 4 districts in the Khatlon region were selected to implement the program. All Rural Health Centers (RHCs) in these seven districts are covered by the program. Nine additional district (two in Sughd and seven in Khatlon) were selected as control districts. The selection of the control districts was guided by geographical proximity to treatment districts and similarity in terms of number of health facilities and doctors per capita. The districts were also selected such that the number of RHCs in treatment and control groups in each region would be similar. Within the chosen 16 districts (treatment and control districts), clusters consisting of a RHC and its subsidiary Health Houses were randomly assigned to implement Collaborative Quality Improvement, Citizen Score Cards, or neither of these two interventions. The randomization was blocked by district. In sum, RHCs were assigned into six study arms. The goal of the Facility-based survey is to measure multiple dimensions of quality of care and collect detailed information on key aspects of facility functioning. Household surveys are primarily used to measure health service coverage at the population level as well as select health outcome indicators measured through anthropometry or tests. The surveys also collect broader data on the health of the households, health seeking behaviors and barriers to use of health services. In addition, PBF and other administrative data would be used to track outcomes over time in the treatment groups 1-3 (the ones receiving performance-based payments). The baseline survey was implemented prior to the implementation of PBF in the 7 study treatment districts and a follow-up survey (endline) is planned to take place after three years of project implementation. The survey is largely based on the HRITF instruments that were modified to the Tajik and project context.

The 2018 endline survey of the impact evaluation (IE) for Health Performance-Based Financing (PBF) in Tajikistan sought to ascertain: (i) the impact and cost-effectiveness of the PBF model implemented in Tajikistan; and (ii) whether PBF is more effective or cost-effective if implemented in conjunction with additional low-cost interventions (Collaborative Quality Improvement, Citizen Report Cards). The results from the IE will help inform the Ministry of Health on whether PBF should be scaled-up to additional PHC level institutions in other regions. The Collaborative Quality Improvement intervention responds to policy concerns that performance incentives may not produce the desired improvements if providers lack the necessary competencies to inform decisions and knowledge. The Citizen Report Card attempts to improve the effectiveness of PBF by strengthening the 'short route' of accountability (e.g., by increasing accountability of health facilities to their local constituents). Since PBF, collaborative quality improvement (CQI), and citizen report cards (CRC) have never been implemented on a large scale in Tajikistan, it is to be expected that the results from the IE will be useful for designing national PHC policy in Tajikistan, and that they will also contribute to the larger body of knowledge on these interventions. The IE employs both difference-in-difference and experimental approaches to identify the impact of the different combinations of interventions. Assignment to PBF was not random. Three districts in the Sughd region and four districts in the Khatlon region were selected to implement the program. All Rural Health Centers (RHCs) in these seven districts are covered by the program. Nine additional districts (two in Sughd and seven in Khatlon) were selected as control districts. The selection of the control districts was guided by geographical proximity to treatment districts and similarity in terms of number of health facilities and doctors per capita. The districts were also selected such that the number of RHCs in treatment and control groups in each region would be similar. Within the chosen 16 districts (treatment and control districts), clusters consisting of an RHC and its subsidiary Health Houses were randomly assigned to implement Collaborative Quality Improvement, Citizen Score Cards, or neither of these two interventions. The randomization was blocked by district. In sum, RHCs were assigned into six study arms. The goal of the facility-based survey is to measure multiple dimensions of quality of care and collect detailed information on key aspects of facility functioning. Household surveys are primarily used to measure health service coverage at the population level as well as select health outcome indicators measured through anthropometry or tests. The surveys also collect broader data on the health of the households, health seeking behaviors and barriers to use of health services. In addition, PBF and other administrative data would be used to track outcomes over time in the treatment groups 1-3 (the ones receiving performance-based payments). The endline (follow-up) survey took place three years after project implementation. The survey is largely based on the HRITF instruments that were modified to the Tajik and project context.

Forecast: Population Per Medical Doctors Graduates in France 2024 - 2028 Discover more data with ReportLinker!

Forecast: Population Per Medical Doctors Graduates in the UK 2024 - 2028 Discover more data with ReportLinker!

ObjectiveMany jurisdictions lack comprehensive population-based antibiotic use data and rely on third party companies, most commonly IQVIA. Our objective was to validate the accuracy of the IQVIA Xponent antibiotic database in identifying high prescribing physicians compared to the reference standard of a highly accurate population-wide database of outpatient antimicrobial dispensing for patients ≥65 years.MethodsWe conducted this study between 1 March 2016 and 28 February 2017 in Ontario, Canada. We evaluated the agreement and correlation between the databases using kappa statistics and Bland-Altman plots. We also assessed performance characteristics for Xponent to accurately identify high prescribing physicians with sensitivity, specificity, positive predictive value (PPV), and negative predictive value.ResultsWe included 9,272 physicians. The Xponent database has a specificity of 92.4% (95%CI 92.0%-92.8%) and PPV of 77.2% (95%CI 76.0%-78.4%) for correctly identifying the top 25th percentile of physicians by antibiotic volume. In the sensitivity analysis, 94% of the top 25th percentile physicians in Xponent were within the top 40th percentile in the reference database. The mean number of antibiotic prescriptions per physician were similar with a relative difference of -0.4% and 2.7% for female and male patients, respectively. The error was greater in rural areas with a relative difference of -8.4% and -5.6% per physician for female and male patients, respectively. The weighted kappa for quartile agreement was 0.68 (95%CI 0.67–0.69).ConclusionWe validated the IQVIA Xponent antibiotic database to identify high prescribing physicians for patients ≥65 years, and identified some important limitations. Collecting accurate population-based antibiotic use data will remain vital to global antimicrobial stewardship efforts.

Dear NHS Business Services Authority, Please can you supply *referenced data: 1 - The number of free prescriptions claimed per financial year for the last 4 - 10years & therefore paid for by the government (taxpayers). 2 - Clarify if you include PPC (pre payment certificates) as 'free' prescriptions i.e are included in the above figure if so then give a figure of PPCs. 3 - The cost of the free prescriptions (i.e 2billion x £9.90 = £19.8 billion) 4 - The average prescription number per person per age range. i.e pensioners claim the most yet get free when could be moved to PPCs. Condition- Please supply what data / figures you can, if you are not able to meet a certain request please use common sense. i.e - If you only have data for 2years then supply that etc. Response I can confirm that the NHSBSA holds the information you have requested, and a copy of the information is attached. Please read the below notes to ensure correct understanding of the data. For Questions 1 - 3 please refer to the supporting management information associated with the Prescription Cost Analysis statistical release: See https://www.nhsbsa.nhs.uk/statistical-collections/prescription-cost-analysis-england/prescription-cost-analysis-england-202425 Specifically: "Exemption Category management information (Excel)" https://nhsbsa-opendata.s3.eu-west-2.amazonaws.com/pca/pca_exemption_categories_2024_25_v001.xlsx Question 1: The table described above provides data on prescriptions where no charge was paid at the point of dispensing is available as a supplementary report to the Prescription Cost Analysis data. Question 2: This table gives the reason - recorded in our system as an 'Exemption category' - that a charge was not paid at the point of dispensing where our systems were able to record it - those reasons will include cases where a patient held a valid pre-payment certificate. Please note that exemptions from the charge are handled at prescription form level, some item level charges such as Free of Charge Contraceptives are not recorded in the itemised data. Question 3: The report gives 'costs' on two bases in the 'Exemption_categories' worksheet and the 'Estimated_charges' worksheet. Please refer to the notes for mode detail on the meaning of these different types of 'cost'. Question 4: The attached table, "FOI02921_Q4.csv", includes the number of prescription items and number of identified patients by age band and items per patient, where these are recorded in our database. Please note that whilst usually one charge per item is applicable this is not always the case for prescriptions with different strengths or for combination products or for appliances that come with accessories or as kits. The applicable number of charges is not held on record for prescription items where no charges were paid. We don't hold data on 'pensioners' so we've used a standard 5 year age band grouping that we hold in our database. NHS Prescription Services process prescriptions for Pharmacy Contractors, Appliance Contractors, Dispensing Doctors and Personal Administration with information then used to make payments to pharmacists and appliance contractors in England for prescriptions dispensed in primary care settings (other arrangements are in place for making payments to Dispensing Doctors and Personal Administration). This involves processing over 1 billion prescription items and payments totalling over £9 billion each year. The information gathered from this process is then used to provide information on costs and trends in prescribing in England and Wales to over 25,000 registered NHS and Department of Health and Social Care users. • Prescription items dispensed in England and submitted to NHSBSA for reimbursement. Note some items could have been prescribed in other parts of the UK. • Prescription items supplied by pharmacies as part of the 'pharmacy first clinical pathways' advanced service under the associated Patient Group Directions.

ObjectiveTo explore influential factors contributing to the choice of primary care facilities (PCFs) for the initial treatment among rural and urban residents in Southwestern China.MethodsA face-to-face survey was conducted on a multistage stratified random sample of 456 rural and 459 urban residents in Sichuan Province from January to August in 2014. A structured questionnaire was used to collect data on residents’ characteristics, provider of initial treatment and principal reason for the choice. Multivariate logistic regression was performed to identify factors associated with choosing PCFs for the initial treatment.ResultsThe result showed that 65.4% of the rural residents and 50.5% of the urban residents chose PCFs as their initial contact for medical care. Among both rural and urban residents, the principal reason for choosing medical institutions for the initial treatment was convenience (42.3% versus 40.5%, respectively), followed by high quality of medical care (26.5% versus 29.4%, respectively). Compared to rural residents, urban residents were more likely to value trust in doctors and high quality of medical care but were less likely to value the insurance designation status of the facilities. Logistic regression analysis showed that both rural and urban residents were less likely to choose PCFs for the initial treatment if they lived more than 15 minutes (by walk) from the nearest facilities (rural: OR = 0.15, 95%CI = 0.09–0.26; urban: OR = 0.19, 95%CI = 0.10–0.36), had fair (rural: OR = 0.49, 95%CI = 0.26–0.92; urban: OR = 0.31, 95%CI = 0.15–0.64) or poor (rural: OR = 0.14, 95%CI = 0.07–0.30; urban: OR = 0.22, 95%CI = 0.11–0.44) self-reported health status. Among rural residents, attending college or higher education (OR = 0.21, 95%CI = 0.08–0.59), being retired (OR = 0.90, 95%CI = 0.44–1.84) and earning a per capita annual income of household of 10,000–29,999 (OR = 0.24, 95%CI = 0.11–0.52) and 30,000–49,999 (OR = 0.26, 95%CI = 0.07–0.92) were associated with lower rates of seeking care at PCFs.ConclusionEfforts should be made to improve the accessibility of PCFs and to upgrade the services capability of PCFs both in rural and urban areas in China. At the same time, resources should be prioritized to residents with poorer self-reported health status, and rural residents who retire or have better education and higher income levels should be taken into account.

This statistic shows a ranking of the estimated current healthcare spending per capita in 2020 in Africa, differentiated by country. 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 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 average number of physicians per 1,000 inhabitants in Colombia was forecast to continuously increase between 2024 and 2029 by in total 0.2 physicians (+8.2 percent). After the seventh consecutive increasing year, the number of physicians is estimated to reach 2.68 physicians and therefore a new peak in 2029. Depicted here is the average number of physicians per one thousand people. Thereby physicians include medical specialists as well as general practitioners. A data point thereby denotes the weighted average across the depicted geographical unit.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).

The current healthcare spending per capita in Ghana was forecast to continuously increase between 2024 and 2029 by in total 20.5 U.S. dollars (+22.15 percent). After the fourth consecutive increasing year, the spending is estimated to reach 113.05 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 Ivory Coast and Nigeria.

The average number of hospital beds available per 1,000 people in Ghana was forecast to continuously decrease between 2024 and 2029 by in total 0.01 beds (-1.54 percent). The number of available beds per 1,000 people is estimated to amount to 0.64 beds in 2029. Depicted is the number of hospital beds per capita in the country or region at hand. As defined by World Bank this includes inpatient beds in general, specialized, public and private hospitals as well as rehabilitation centers.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 average number of hospital beds available per 1,000 people in countries like Ivory Coast and Senegal.

The number of physicians in Argentina was forecast to continuously increase between 2024 and 2029 by in total 1.5 thousand physicians (+0.86 percent). According to this forecast, in 2029, the number of physicians will have increased for the sixth consecutive year to 176.63 thousand physicians. Depicted here is the estimated number of physicians in the geographical unit at hand. Thereby physicians include medical specialists as well as general practitioners.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 number of physicians in countries like Paraguay and Uruguay.

The number of physicians in Ghana was forecast to continuously increase between 2024 and 2029 by in total 2.1 thousand physicians (+26.92 percent). After the tenth consecutive increasing year, the number of physicians is estimated to reach 9.94 thousand physicians and therefore a new peak in 2029. Depicted here is the estimated number of physicians in the geographical unit at hand. Thereby physicians include medical specialists as well as general practitioners.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 number of physicians in countries like Ivory Coast and Nigeria.

The number of hospital beds in Ghana was forecast to continuously increase between 2024 and 2029 by in total three thousand beds (+9.58 percent). After the fifteenth consecutive increasing year, the number of hospital beds is estimated to reach 34.29 thousand beds and therefore a new peak in 2029. Notably, the number of hospital beds of was continuously increasing over the past years.Depicted is the estimated total number of hospital beds in the country or region at hand.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 number of hospital beds in countries like Nigeria and Ivory Coast.