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.

Bulgaria BG: External Health Expenditure: % of Current Health Expenditure data was reported at 0.564 % in 2021. This records a decrease from the previous number of 2.029 % for 2020. Bulgaria BG: External Health Expenditure: % of Current Health Expenditure data is updated yearly, averaging 1.296 % from Dec 2020 (Median) to 2021, with 2 observations. The data reached an all-time high of 2.029 % in 2020 and a record low of 0.564 % in 2021. Bulgaria BG: External Health Expenditure: % of Current Health Expenditure data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bulgaria – Table BG.World Bank.WDI: Social: Health Statistics. 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.;World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database). The data was retrieved on April 4, 2025.;Weighted average;

Bulgaria BG: External Health Expenditure Per Capita: Current PPP data was reported at 0.000 Intl $ mn in 2021. This records a decrease from the previous number of 0.000 Intl $ mn for 2020. Bulgaria BG: External Health Expenditure Per Capita: Current PPP data is updated yearly, averaging 0.000 Intl $ mn from Dec 2020 (Median) to 2021, with 2 observations. The data reached an all-time high of 0.000 Intl $ mn in 2020 and a record low of 0.000 Intl $ mn in 2021. Bulgaria BG: External Health Expenditure Per Capita: Current PPP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bulgaria – Table BG.World Bank.WDI: Social: Health Statistics. Current external expenditures on health per capita expressed in international dollars at purchasing power parity. External sources are composed of direct foreign transfers and foreign transfers distributed by government encompassing all financial inflows into the national health system from outside the country.;World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database). The data was retrieved on April 4, 2025.;Weighted average;

Analysis of ‘Life Expectancy (WHO)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/kumarajarshi/life-expectancy-who on 28 January 2022.

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

Context

Although there have been lot of studies undertaken in the past on factors affecting life expectancy considering demographic variables, income composition and mortality rates. It was found that affect of immunization and human development index was not taken into account in the past. Also, some of the past research was done considering multiple linear regression based on data set of one year for all the countries. Hence, this gives motivation to resolve both the factors stated previously by formulating a regression model based on mixed effects model and multiple linear regression while considering data from a period of 2000 to 2015 for all the countries. Important immunization like Hepatitis B, Polio and Diphtheria will also be considered. In a nutshell, this study will focus on immunization factors, mortality factors, economic factors, social factors and other health related factors as well. Since the observations this dataset are based on different countries, it will be easier for a country to determine the predicting factor which is contributing to lower value of life expectancy. This will help in suggesting a country which area should be given importance in order to efficiently improve the life expectancy of its population.

Content

The project relies on accuracy of data. The Global Health Observatory (GHO) data repository under World Health Organization (WHO) keeps track of the health status as well as many other related factors for all countries The data-sets are made available to public for the purpose of health data analysis. The data-set related to life expectancy, health factors for 193 countries has been collected from the same WHO data repository website and its corresponding economic data was collected from United Nation website. Among all categories of health-related factors only those critical factors were chosen which are more representative. It has been observed that in the past 15 years , there has been a huge development in health sector resulting in improvement of human mortality rates especially in the developing nations in comparison to the past 30 years. Therefore, in this project we have considered data from year 2000-2015 for 193 countries for further analysis. The individual data files have been merged together into a single data-set. On initial visual inspection of the data showed some missing values. As the data-sets were from WHO, we found no evident errors. Missing data was handled in R software by using Missmap command. The result indicated that most of the missing data was for population, Hepatitis B and GDP. The missing data were from less known countries like Vanuatu, Tonga, Togo, Cabo Verde etc. Finding all data for these countries was difficult and hence, it was decided that we exclude these countries from the final model data-set. The final merged file(final dataset) consists of 22 Columns and 2938 rows which meant 20 predicting variables. All predicting variables was then divided into several broad categories:​Immunization related factors, Mortality factors, Economical factors and Social factors.

Acknowledgements

The data was collected from WHO and United Nations website with the help of Deeksha Russell and Duan Wang.

Inspiration

The data-set aims to answer the following key questions: 1. Does various predicting factors which has been chosen initially really affect the Life expectancy? What are the predicting variables actually affecting the life expectancy? 2. Should a country having a lower life expectancy value(<65) increase its healthcare expenditure in order to improve its average lifespan? 3. How does Infant and Adult mortality rates affect life expectancy? 4. Does Life Expectancy has positive or negative correlation with eating habits, lifestyle, exercise, smoking, drinking alcohol etc. 5. What is the impact of schooling on the lifespan of humans? 6. Does Life Expectancy have positive or negative relationship with drinking alcohol? 7. Do densely populated countries tend to have lower life expectancy? 8. What is the impact of Immunization coverage on life Expectancy?

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

Bulgaria BG: External Health Expenditure Per Capita: Current Price data was reported at 0.000 USD mn in 2021. This records a decrease from the previous number of 0.000 USD mn for 2020. Bulgaria BG: External Health Expenditure Per Capita: Current Price data is updated yearly, averaging 0.000 USD mn from Dec 2020 (Median) to 2021, with 2 observations. The data reached an all-time high of 0.000 USD mn in 2020 and a record low of 0.000 USD mn in 2021. Bulgaria BG: External Health Expenditure Per Capita: Current Price data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bulgaria – Table BG.World Bank.WDI: Social: Health Statistics. Current external expenditures on health per capita expressed in current US dollars. External sources are composed of direct foreign transfers and foreign transfers distributed by government encompassing all financial inflows into the national health system from outside the country.;World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database). The data was retrieved on April 4, 2025.;Weighted average;

Analysis of ‘Supply Chain Shipment Pricing Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/e7707c1f-2856-4df6-8d0c-ed1ba8a3cd91 on 12 February 2022.

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

This data set provides supply chain health commodity shipment and pricing data. Specifically, the data set identifies Antiretroviral (ARV) and HIV lab shipments to supported countries. In addition, the data set provides the commodity pricing and associated supply chain expenses necessary to move the commodities to countries for use. The dataset has similar fields to the Global Fund's Price, Quality and Reporting (PQR) data. PEPFAR and the Global Fund represent the two largest procurers of HIV health commodities. This dataset, when analyzed in conjunction with the PQR data, provides a more complete picture of global spending on specific health commodities. The data are particularly valuable for understanding ranges and trends in pricing as well as volumes delivered by country. The US Government believes this data will help stakeholders make better, data-driven decisions. Care should be taken to consider contextual factors when using the database. Conclusions related to costs associated with moving specific line items or products to specific countries and lead times by product/country will not be accurate.

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

With the recent Ebola epidemic, the flaws in Liberia’s medical infrastructure have been made painfully obvious. Liberia, a country of four million people, has only 37 practicing doctors according to health officials. This is evidence of a serious lack in the availability of medical services to the majority of Liberians. An American gynecologist who visited the country in 2012 to provide services with a team from the Mt. Sinai Hospital observed families of hospital patients supplying their own food and bed linens due to the medical facility they were working in lacking funds for basic necessities. The root issue at the heart of many of Liberia’s woes stems from the long civil war. In addition to damaging the medical infrastructure, the country’s only medical school was forced to close for long periods of time, resulting in medical students taking an average eight years to graduate. There has been a serious push for reform and revitalization with medical facilities being rebuilt and medical students now on track to spend only three years in school. Liberia is facing a number of issues, and prior to the current epidemic has not prioritized health expenditures. The government spends an estimated 16.8 percent of their GDP, the lowest in the world, on healthcare. The average GDP spending on healthcare systems in sub-Saharan Africa is ~50 percent. Liberia’s healthcare system is highly dependent on international aid. Donors finance 50 percent of total health expenditures. Approximately 80 percent of all health services are provided by non-governmental organizations (NGOs) and will continue to be so for the foreseeable future. However, the Ministry of Health and Social Welfare has been working with NGOs such as Health Systems 20/20 to improve their existing infrastructure. Attribute Table Field DescriptionsISO3 - International Organization for Standardization 3-digit country code ADM0_NAME - Administration level zero identification / name ADM1_NAME - Administration level one identification / name ADM2_NAME - Administration level two identification / name NAME - Name of health facility TYPE1 - Primary classification in the geodatabase TYPE2 - Secondary classification in the geodatabase CITY - City location available SPA_ACC - Spatial accuracy of site location (1 – high, 2 – medium, 3 – low) COMMENTS - Comments or notes regarding themedical facility SOURCE_DT - Source one creation date SOURCE - Source one SOURCE2_DT - Source two creation date SOURCE2 - Source two CollectionThe feature class was generated utilizing data from OpenStreetMap, Wikimapia, GeoNames and other sources. OpenStreetMap is a free worldwide map, created by crowd-sourcing. Wikimapia is open-content mapping focused on gathering all geographical objects in the world. GeoNames is a geographical places database maintained and edited by the online community. Consistent naming conventions for geographic locations were attempted but name variants may exist, which can include historical or less widespread interpretations.The data included herein have not been derived from a registered survey and should be considered approximate unless otherwise defined. While rigorous steps have been taken to ensure the quality of each dataset, DigitalGlobe is not responsible for the accuracy and completeness of data compiled from outside sources.Sources (HGIS)Aizenman, Nurith and Beemsterboer, Nicole. “Why Patients Aren’t Coming to Liberia’s Redemption Hospital.” August 27, 2014. Accessed September 26, 2014. www.npr.org.“Liberia: ArcelorMittal Folds Partly – Terminates Expansion Contract.” All Africa. August 14, 2013. Accessed September 26, 2014. allafrica.com. Cohen, Elizabeth. “Ebola Patients Left to Lie on the Ground.” CNN. September 23, 2014. Accessed September 26, 2014. www.cnn.com.“Kingdom Care Medical Center Reaches Rural Communities with Health Care.” Daily Observer. January 28, 2014. Accessed September 26, 2014. www.liberianobserver.com. DigitalGlobe, "DigitalGlobe Imagery Archive." Accessed September 24, 2014.“Eternal Love Winning Africa: ELWA Hospital.” Eternal Love Winning Africa. January 2014. Accessed September 26, 2014. www.elwaministries.org.Freeman, Colin. “One Patient in a 200-bed Hospital: How Ebola has Devastated Liberia’s Health System.” The Telegraph. August 15, 2014. Accessed September 26, 2014. www.telegraph.co.uk.“Lewin Reaches Out to River Gee, Maryland.” Gale Global Issues. March 4, 2013. . Accessed September 26, 2014. find.galegroup.com. Gbelewala, Korboi. “Liberia: Health Offical – Ebola Death Toll Hits 11 in Lofa.” All Africa. June 24, 2014. Accessed September 26, 2014. allafrica.com. GeoNames, "Liberia." September 23, 2014. Accessed September 23, 2014. www.geonames.org.Google, September 2014. Accessed September 2014. www.google.com.Kollie, Namotee P.M. “Liberia: C.B. Dunbar Hospital Receives Medical Supplies.” September 27, 2013. Accessed September 26, 2014. allafrica.com.“MSF Hands Over Last Hospitals to Ministry of Health after 20 Years of Emergency Aid in Liberia.” Medecins Sans Frontieres. June 25, 2010. Accessed September 26, 2014. www.msf.org. Nah, Vivian M. and Johnson, Obediah. “Liberia: Ebola Kills Woman at Duside Hospital in Firestone.” All Africa. April 4, 2014. Accessed September 26, 2014. allafrica.com. “Catholic Hospital Director Dies of Ebola in Liberia.” National Catholic Register. August 05, 2014. Accessed September 26, 2014. www.ncregister.com.OpenStreetMap, "Liberia." September 2014. Accessed September 18, 2014. www.openstreetmap.org.Senkpeni, Alpha Daffae. “No Ebola Gears for Clinic in Grand Bassa District #2.” Front Page Africa. August 12, 2014. Accessed September 26, 2014. www.frontpageafricaonline.com. “Seventh-day Adventist Cooper Hospital” Seventh-Day Adventist Church. November 18, 2004. Accessed September 26, 2014. www.adventistdirectory.org.“St. Benedict Menni Rehabilitation Centre, Liberia.” Sisters Hospitallers. January 2014. Accessed September 26, 2014. www.sistershospitallers.org. “Liberia – SOS Medical and Social Centres.” SOS Children’s Villages. January 2014. Accessed September 26, 2014. www.sos-medical-centres.org.“Liberia.” Sustainable Marketplace. January 2014. Accessed September 26, 2014. liberia.buildingmarkets.org. “Reconstruction of the Vinjama Hospital in Liberia.” Swiss Agency for Development and Cooperation (SDC). January 2014. Accessed September 26, 2014. www.sdc.admin.ch. Verdier, Lewis S. “Liberia: TB On the Rise in Pleebo.” All Africa. March 28, 2013. Accessed September 26, 2014. allafrica.com.Wikimapia, "Liberia." September 2014. Accessed September 22, 2014. wikimapia.org.“Snapper Hill Clinic.” Word Press. November 12, 2012. Accessed September 26, 2014. jbloodnc.wordpress.com.Sources (Metadata)Neporent, Liz. "Liberia's Medical Conditions Dire Even Before Ebola Outbreak." ABC News. August 4, 2014. Accessed October 3, 2014. abcnews.go.com."Liberia." Health Systems Strengthening: Where We Work:. January 1, 2014. Accessed October 3, 2014. www.healthsystems2020.org."Financing Liberia's Health Care." Health Systems Strengthening: News:. February 13, 2012. Accessed October 3, 2014. www.healthsystems2020.org.UNCLASSIFIED

This data set provides supply chain health commodity shipment and pricing data. Specifically, the data set identifies Antiretroviral (ARV) and HIV lab shipments to supported countries. In addition, the data set provides the commodity pricing and associated supply chain expenses necessary to move the commodities to countries for use. The dataset has similar fields to the Global Fund's Price, Quality and Reporting (PQR) data. PEPFAR and the Global Fund represent the two largest procurers of HIV health commodities. This dataset, when analyzed in conjunction with the PQR data, provides a more complete picture of global spending on specific health commodities. The data are particularly valuable for understanding ranges and trends in pricing as well as volumes delivered by country. The US Government believes this data will help stakeholders make better, data-driven decisions. Care should be taken to consider contextual factors when using the database. Conclusions related to costs associated with moving specific line items or products to specific countries and lead times by product/country will not be accurate.

description: This data set provides supply chain health commodity shipment and pricing data. Specifically, the data set identifies Antiretroviral (ARV) and HIV lab shipments to supported countries. In addition, the data set provides the commodity pricing and associated supply chain expenses necessary to move the commodities to countries for use. The dataset has similar fields to the Global Fund's Price, Quality and Reporting (PQR) data. PEPFAR and the Global Fund represent the two largest procurers of HIV health commodities. This dataset, when analyzed in conjunction with the PQR data, provides a more complete picture of global spending on specific health commodities. The data are particularly valuable for understanding ranges and trends in pricing as well as volumes delivered by country. The US Government believes this data will help stakeholders make better, data-driven decisions. Care should be taken to consider contextual factors when using the database. Conclusions related to costs associated with moving specific line items or products to specific countries and lead times by product/country will not be accurate.; abstract: This data set provides supply chain health commodity shipment and pricing data. Specifically, the data set identifies Antiretroviral (ARV) and HIV lab shipments to supported countries. In addition, the data set provides the commodity pricing and associated supply chain expenses necessary to move the commodities to countries for use. The dataset has similar fields to the Global Fund's Price, Quality and Reporting (PQR) data. PEPFAR and the Global Fund represent the two largest procurers of HIV health commodities. This dataset, when analyzed in conjunction with the PQR data, provides a more complete picture of global spending on specific health commodities. The data are particularly valuable for understanding ranges and trends in pricing as well as volumes delivered by country. The US Government believes this data will help stakeholders make better, data-driven decisions. Care should be taken to consider contextual factors when using the database. Conclusions related to costs associated with moving specific line items or products to specific countries and lead times by product/country will not be accurate.

The COVID-19 pandemic and the resulting Large-Scale Social Restrictions (PSBB) have significantly disrupted routine healthcare services, particularly in high TB burden countries. Despite initial expectations that the private health sector would lead in addressing TB, preliminary data suggests that the sector has shrunk or collapsed in many areas. Private facilities struggled to stay open during PSBB, and providers were reluctant to treat people with respiratory symptoms. Private healthcare costs have soared, especially for hospitalizations. Through this project, we were able to measure pre-treatment costs and factors associated with those costs from the perspective of patients during the COVID-19 pandemic in Bandung, Indonesia. It was found that the median total pre-treatment cost was $35.45 with the highest median cost experienced by participants from private hospitals. The rapid antigen and PCR for SARS-CoV-2 emerged as additional medical costs among 26% of participants recruited in private hospitals. Several factors are associated with higher pre-treatment costs including visiting more than 6 providers before diagnosis, presenting first at a private hospital and private practitioners, and being diagnosed in the private health sector. During the COVID-19 pandemic, people with TB faced significant out-of-pocket costs for diagnosis and treatment, highlighting the importance of early detection and identification in reducing pre-diagnostic TB costs.

"Ensure healthy lives and promote well-being for all at all ages : Some progress has been made against key mortality measures. Maternal mortality ratios have already fallen below the 2030 target in three-quarters of Pacific countries and territories, and one-half have achieved the under-five mortality rate target of fewer than 25 deaths per 100,000; The increasing burden of non-communicable diseases, both with respect to the risk of premature mortality and health care costs, is the dominant health issue in the Pacific region. A mixed pattern is found in the two lifestyle risk factors of alcohol and smoking, with three Pacific countries featuring among the top ten world countries in prevalence of current tobacco use among persons aged 15 years and older; Health worker density remains below WHO guidelines in most countries in the region; Malaria is still present in three countries (PNG, Solomon Islands and Vanuatu), although the incidence is decreasing due to awareness and preventative measures.

Find more Pacific data on PDH.stat.

IntroductionAs high out-of-pocket healthcare expenses pose heavy financial burden on the families, Government of India is considering a variety of financing and delivery options to universalize health care services. Hence, an estimate of the cost of delivering universal health care services is needed. MethodsWe developed a model to estimate recurrent and annual costs for providing health services through a mix of public and private providers in Chandigarh located in northern India. Necessary health services required to deliver good quality care were defined by the Indian Public Health Standards. National Sample Survey data was utilized to estimate disease burden. In addition, morbidity and treatment data was collected from two secondary and two tertiary care hospitals. The unit cost of treatment was estimated from the published literature. For diseases where data on treatment cost was not available, we collected data on standard treatment protocols and cost of care from local health providers. ResultsWe estimate that the cost of universal health care delivery through the existing mix of public and private health institutions would be INR 1713 (USD 38, 95%CI USD 18–73) per person per annum in India. This cost would be 24% higher, if branded drugs are used. Extrapolation of these costs to entire country indicates that Indian government needs to spend 3.8% (2.1%–6.8%) of the GDP for universalizing health care services. ConclusionThe cost of universal health care delivered through a combination of public and private providers is estimated to be INR 1713 per capita per year in India. Important issues such as delivery strategy for ensuring quality, reducing inequities in access, and managing the growth of health care demand need be explored.

The levels and distributions of the incidence of catastrophic health expenditure (%) by year; budget share method (SDG indicator 3.8.2).

The levels and distributions of the incidence of catastrophic health expenditure (%) by year; normative food, housing, and utilities method (40% threshold).

Three pages of this questionnaire were devoted to physical activity, the main focus of the project. Both previously validated and newly developed questions were used in order to estimate energy expenditure and to distinguish between constant low-level activity and short-term peak activity. We used a new rating scale for self-reports of time spent on different intensity levels of physical activity (and inactivity) during a typical day, allowing estimation of total energy expenditure on an interval scale level. This scale has been shown to be both valid and reliable. Seven pages contained questions about diet, using a 63-item validated semi-quantitative food frequency questionnaire that allows estimation of total energy intake and calorie adjustment. Supplementary questions were asked about intake of fried food, detailed pattern of alcohol intake, dietary supplements and use of health food preparations. Two pages had questions about anthropometrical measures, including height, weight (birth weight, current weight, and weight fluctuations), waist and hip measures (allowing calculations of BMI, lean body mass, waist-hip ratio). Further, three pages were allocated to questions about various background and possibly confounding factors such as country of birth, environment during childhood and adolescence, birthplace of parents, own education, type of employment. Two pages were about smoking (including passive smoking) and snuff dipping habits. One page was dedicated to vaccination history, two pages to medical history, two pages to pharmacological history. Two pages were about sun and UV exposure, possible use of sunbeds, and type of complexion. Five pages were spent on questions concerning the psychosocial history, including validated sets of questions about demands and autonomy at work, life events, self-perceived health, social support, as well as duration and quality of sleep. Sleep disturbances were assessed using a modified version of the Karolinska Sleep Questionnaire (KSQ), a widely used tool to assess quality and restorative function of sleep. One page was about the use of mobile telephones. Two pages were devoted to questions specific for women (age at menarche, parity, infertility, menstruation, menopause, use of oral contraceptives and hormone replacement therapy). Essentially complete follow-up has since been attained through multiple record linkages, using the individually unique national registration numbers (NRNs) to ensure exact linkages. We have linked the cohort to Statistics Sweden’s Register of the Total Population and Population Changes, and to the Patient Register, Cancer Register, Causes of Death Register, and Prescribed Drug Register from the National Board of Health and Welfare, permitting accurate tracking of vital and health status of all cohort members. Presently, we have complete follow-up through 2010. We are currently applying for new linkages, which will allow us to have information updated through 2012. Purpose: In September 1997, the Swedish Cancer Society organized a four-day national fund-raising event, the National March, in almost 3,600 cities and villages around the country. In conjunction with this event, we established the Swedish National March Cohort. The cohort consists of 43,880 well-motivated men and women who participated in the National March in support of the Swedish Cancer Society. With a 36-page questionnaire about lifestyle, exposure was assessed in much greater detail than in almost any other cohort. Essentially complete follow-up is attained through multiple linkages with high-quality Swedish registers. I samband med Cancerfondens Riksmarsch 1997 fyllde 43 880 svenskar i ett detaljerat 36-sidigt frågeformulär om livsstil och hälsa en sk prospektiv kohortstudie. Deltagarna gav samtidigt sitt samtycke till framtida uppföljning medelst samkörningar med de högkvalitativa svenska befolknings- och hälsoregistren. Kohorten, unik i sitt slag med tanke på detaljrikedomen för både exponering och utfall, har tillräckligt med fall för att ge den statistiska styrka som behövs för att kunna studera livsstilens betydelse och dess association för många av våra vanligaste sjukdomar. Målet är att kunna omsätta kunskapen vi får fram, till rekommendationer och förebyggande åtgärder. Syfte: I september 1997 organiserade den svenska Cancerfonden ett nationellt insamlingsevent, Riksmarschen, som pågick under fyra dagar i nästan 3 600 städer och byar runt om i landet. I samband med detta etablerades Riksmarschkohorten, the National March Cohort. Kohorten består av 43 880 män och kvinnor som deltog i Riksmarschen för att stödja för den svenska Cancerfonden. Med ett 36-sidigt frågeformulär om livsstil, kan exponering bedömas mycket mer detaljerat än i nästan alla andra kohorten. Uppföljning sker huvudsakligen genom kopplingar till flera svenska register av hög kvalitet. Non-probability

Proportion of Population Pushed Below the 60% Median Consumption Poverty Line By Out-of-Pocket Health Expenditure: % data was reported at 0.820 % in 2018. This records a decrease from the previous number of 0.830 % for 2017. Proportion of Population Pushed Below the 60% Median Consumption Poverty Line By Out-of-Pocket Health Expenditure: % data is updated yearly, averaging 0.630 % from Dec 1999 (Median) to 2018, with 17 observations. The data reached an all-time high of 0.970 % in 1999 and a record low of 0.390 % in 2006. Proportion of Population Pushed Below the 60% Median Consumption Poverty Line By Out-of-Pocket Health Expenditure: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Indonesia – Table ID.World Bank.WDI: Social: Poverty and Inequality. This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the 60% median consumption but because of the expenditures is below the poverty line. 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);Weighted average;This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/].