Graph and download economic data for Value Added by Industry: Educational Services, Health Care, and Social Assistance as a Percentage of GDP (VAPGDPESHS) from Q1 2005 to Q1 2025 about social assistance, value added, health, private industries, education, percent, services, private, industry, GDP, and USA.

I discuss the health transition in the United States, bringing new data to bear on health indicators and investigating the changing relationship between health, income, and the environment. I argue that scientific advances played an outsize role and that health improvements were largest among the poor. Health improvements were not a precondition for modern economic growth. The gains to health are largest when the economy has moved from "brawn" to "brains" because this is when the wage returns to education are high, leading the healthy to obtain more education. More education may improve use of health knowledge, producing a virtuous cycle. (JEL H51, I10, J13, N31, N32)

In 2023, U.S. national health expenditure as a share of its gross domestic product (GDP) reached 17.6 percent, this was an increase on the previous year. The United States has the highest health spending based on GDP share among developed countries. Both public and private health spending in the U.S. is much higher than other developed countries. Why the U.S. pays so much moreWhile private health spending in Canada stays at around three percent and in Germany under two percent of the gross domestic product, it is nearly nine percent in the United States. Another reason for high costs can be found in physicians’ salaries, which are much higher in the U.S. than in other wealthy countries. A general practitioner in the U.S. earns nearly twice as much as the average physician in other high-income countries. Additionally, medicine spending per capita is also significantly higher in the United States. Finally, inflated health care administration costs are another of the predominant factors which make health care spending in the U.S. out of proportion. It is important to state that Americans do not pay more because they have a higher health care utilization, but mainly because of higher prices. Expected developmentsBy 2031, it is expected that health care spending in the U.S. will reach nearly one fifth of the nation’s gross domestic product. Or in dollar-terms, health care expenditures will accumulate to about seven trillion U.S. dollars in total.

In 2024, the U.S. GDP increased from the previous year to about 29.18 trillion U.S. dollars. Gross domestic product (GDP) refers to the market value of all goods and services produced within a country. In 2024, the United States has the largest economy in the world. What is GDP? Gross domestic product is one of the most important indicators used to analyze the health of an economy. GDP is defined by the BEA as the market value of goods and services produced by labor and property in the United States, regardless of nationality. It is the primary measure of U.S. production. The OECD defines GDP as an aggregate measure of production equal to the sum of the gross values added of all resident, institutional units engaged in production (plus any taxes, and minus any subsidies, on products not included in the value of their outputs). GDP and national debt Although the United States had the highest Gross Domestic Product (GDP) in the world in 2022, this does not tell us much about the quality of life in any given country. GDP per capita at purchasing power parity (PPP) is an economic measurement that is thought to be a better method for comparing living standards across countries because it accounts for domestic inflation and variations in the cost of living. While the United States might have the largest economy, the country that ranked highest in terms of GDP at PPP was Luxembourg, amounting to around 141,333 international dollars per capita. Singapore, Ireland, and Qatar also ranked highly on the GDP PPP list, and the United States ranked 9th in 2022.

Kenya KE: Domestic General Government Health Expenditure: % of GDP data was reported at 1.728 % in 2015. This records a decrease from the previous number of 1.802 % for 2014. Kenya KE: Domestic General Government Health Expenditure: % of GDP data is updated yearly, averaging 1.793 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 2.200 % in 2002 and a record low of 1.188 % in 2008. Kenya KE: Domestic General Government Health Expenditure: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Health Statistics. Public expenditure on health from domestic sources as a share of the economy as measured by GDP.; ; World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database).; Weighted average;

Georgia Term Loans: GEL: Economy: Health Care and Social Services data was reported at 107,788.871 GEL th in Jun 2018. This records a decrease from the previous number of 112,366.927 GEL th for May 2018. Georgia Term Loans: GEL: Economy: Health Care and Social Services data is updated monthly, averaging 16,687.426 GEL th from Oct 2003 (Median) to Jun 2018, with 177 observations. The data reached an all-time high of 193,121.833 GEL th in Jul 2015 and a record low of 213.259 GEL th in Oct 2003. Georgia Term Loans: GEL: Economy: Health Care and Social Services data remains active status in CEIC and is reported by National Bank of Georgia . The data is categorized under Global Database’s Georgia – Table GE.KB003: Loans: by Industry.

Note: Blueprint has been retired as of June 15, 2021. This dataset will be kept up for historical purposes, but will no longer be updated. California has a new blueprint for reducing COVID-19 in the state with revised criteria for loosening and tightening restrictions on activities. Every county in California is assigned to a tier based on its test positivity and adjusted case rate for tier assignment. Additionally, a new health equity metric took effect on October 6, 2020. In order to advance to the next less restrictive tier, each county will need to meet an equity metric or demonstrate targeted investments to eliminate disparities in levels of COVID-19 transmission, depending on its size. The California Health Equity Metric is designed to help guide counties in their continuing efforts to reduce COVID-19 cases in all communities and requires more intensive efforts to prevent and mitigate the spread of COVID-19 among Californians who have been disproportionately impacted by this pandemic. Please see https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/COVID19CountyMonitoringOverview.aspx for more information. Also, in lieu of a Data Dictionary, please refer to the detailed explanation of the data columns in Appendix 1 of the above webpage. Because this data is in machine-readable format, the merged headers at the top of the source spreadsheet have not been included: The first 8 columns are under the header "County Status as of Tier Assignment" The next 3 columns are under the header "Current Data Week Tier and Metric Tiers for Data Week" The next 4 columns are under the header "Case Rate Adjustment Factors" The next column is under the header "Small County Considerations" The last 5 columns are under the header "Health Equity Framework Parameters"

As urbanization speeds up, the concept of healthy cities is receiving more focus. This article compares Chongzuo and Nanning in Guangxi with Beijing to assess the development gaps in cities in Guangxi. An indicator system for healthy cities was designed from six dimensions—healthy economy, healthy population, healthy healthcare, healthy environment, healthy facilities, and healthy transportation—and 26 secondary indicators, which were selected from 2005 to 2022, and an improved factor analysis was used to synthesize a healthy city index (HCI). The number of factors was determined by combining characteristic roots and the variance contribution rate, and the HCI was weighted using the entropy-weighted Topsis method. A comprehensive evaluation of the urban health status of these cities was conducted. The results showed that extracting six common factors had the greatest effect, with a cumulative variance contribution rate of 93.83%. Chongzuo city scored higher in the field of healthcare. The healthy environment score of Nanning was relatively high, which may be related to continuous increases in green measures. In terms of the healthy economy dimension, Beijing was far ahead. However, in recent years, the healthy economy level in Chongzuo has increased, and the GDP growth rate has ranked among the highest in Guangxi. In addition, the growth rate of healthy facilities in Nanning was relatively fast and has been greater than that in Chongzuo in recent years, which indicates that the Nanning Municipal Government believes urban construction and municipal supporting facilities are highly important. In terms of healthy transportation, Chongzuo and Nanning scored higher than Beijing. This may be because the transportation in these two cities is convenient and the traffic density is more balanced than that in Beijing, thereby reducing traffic congestion. Chongzuo had the highest score for a healthy population, and a steadily growing population provides the city with stable human resources, which helps promote urban economic and social development. Finally, relevant policy recommendations were put forwards to enhance the health level of the cities.

This study shows a review about the report of Social environment and its influence on economic health of the firm which engages in wholesale and retail trading ofclothes and footwear case.

Laos LA: Domestic General Government Health Expenditure: % of GDP data was reported at 0.988 % in 2015. This records an increase from the previous number of 0.765 % for 2014. Laos LA: Domestic General Government Health Expenditure: % of GDP data is updated yearly, averaging 0.843 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 1.539 % in 2001 and a record low of 0.408 % in 2011. Laos LA: Domestic General Government Health Expenditure: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Laos – Table LA.World Bank: Health Statistics. Public expenditure on health from domestic sources as a share of the economy as measured by GDP.; ; World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database).; Weighted Average;

Graph and download economic data for Economic Policy Uncertainty Index: Categorical Index: Health care (EPUHEALTHCARE) from Jan 1985 to Apr 2025 about healthcare, uncertainty, health, World, and indexes.

The Health Information Technology for Economic and Clinical Health (HITECH) Act was passed as part of the American Recovery and Reinvestment Act (ARRA) to invest in the U.S. health IT infrastructure. The Office of the National Coordinator for Health IT (ONC) received over $2 billion of these HITECH funds, which was granted to health and community organizations across the U.S. This crosswalk provides geographic data for the service areas of two of these HITECH programs: the Health IT Regional Extension Centers (REC) Program and the Beacon Communities Program. This data can be linked to program financial and performance data to map and visualize program data. You can access the data in multiple formats below.

United States - Economic Policy Uncertainty : Categorical : Health care was 594.53679 Index in March of 2025, according to the United States Federal Reserve. Historically, United States - Economic Policy Uncertainty : Categorical : Health care reached a record high of 1030.68062 in April of 2020 and a record low of 6.85732 in December of 1985. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Economic Policy Uncertainty : Categorical : Health care - last updated from the United States Federal Reserve on May of 2025.

This fifteenth Orange County Annual Survey, UCI, examines several topics of recent relevance in Orange County and analyzes social, economic and political trends over time. The survey measures the extent to which Orange County has recovered from the economic recession and the county government's bankruptcy. It does this by analyzing trends in attitudes toward the economy, quality of life, local government, consumer confidence and personal finance. A special focus this year is to better understand attitudes about charity and charitable giving. Finally, it continues to track trends over time in the county's most important problems, transportation, housing and the political climate. The sample size is 1,000 Orange County adult residents.

Graph and download economic data for Per Capita Personal Consumption Expenditures: Services: Health Care for Florida (FLPCEPCHLTHCARE) from 1997 to 2023 about healthcare, health, PCE, consumption expenditures, per capita, FL, consumption, personal, services, and USA.

The data and programs replicate tables and figures from "The Economic Consequences of Increasing Sleep among the Urban Poor", by Bessone, Rao, Schilbach, Schofield, and Toma. Please see the read_me file for additional details.

Data is sourced from various health resources. Data is transformed into a BI format and quality assured. Data is consumed by a dashboard created in Power BI. Four reports exist for this dashboard:1. HIV Prevalence and TB Success RateHIV prevalence amongst women attending antenatal clinics in the Western Cape (2012-2015) by district and yearHIV prevalence amongst women attending antenatal clinics in the province (2012-2015) by province and yearTB Programme Success Rate (2013/14-2018/19) by TB Measure2. Births and Maternal MortalitiesNeonatal in facility (0-28 days) mortality rate (2015/16-2018/19); by years and neonatal death rate in facility and mortality rate by 1,000 live births Facility maternal mortality rate (2002, 2005, 2008, 2011, 2014); by triennia (3 years) deaths by 1,000 live births in WC (incl count of maternal deaths, count of live births, and infant maternal mortality ration)(Child (under 5) and Infant (under 1) mortality rate (2011, 2012, 2013); filter years, Infant/Child age band; Years, District, Births and Deaths by age bandDelivery rate in facility to women under 20 years (2013/14-2018/19); filter by financial year (FY); delivery rate by FY, delivery rate, numerator (births to women <20), denominator (total births)3. Deaths and Life ExpectancyLeading underlying causes of death in the Western Cape (2012-2016) by years and cause of deathYears of life lost (YLL) by cause of death in the WC (2012-2016) by years and YLL cause of deathAverage Life Expectency (LE) at birth (2006, 2011, 2016) by year, province, and gender4. Travel time to facilitiesTravel time taken to health facility by households with expenditure less than R1200-SA (2013-2018); by year, province, and travel time to health facilityTravel time taken to health facility by households with expenditure less than R1200-WC (2013-2018); by year, province, population group, and travel time to health facilityPublication Date2 September 2021LineageData from various sources transformed to a BI format and used to develop dynamic Power BI dashboards reflecting Outcome Indicators: HIV prevalence amongst women attending antenatal clinics in the provinceAll DS-TB (drug-susceptible tuberculosis) client treatment success rateNeonatal in facility (0-28 days) mortality rateFacility maternal mortality rateDelivery rate in facility to women under 20 yearsLife Expectancy (LE)Leading underlying causes of death in the Western CapeTravel time taken to health facility by households with expenditure less than R1200 (SA and WC)Data Source2019 National Antenatal Sentinel HIV Survey, National Department of Health 2021;Annual report 2014/15-2020/21, DOH;District Health Information Systems;Mid-year population estimates, Stats SA; Life Expectancy Stats SA calculations;Mortality and Causes of Death in South Africa 2018, June 2021, Stats SA

Additional file 8. Excluded reviews.

Additional file 7. Data extraction and QA for included reviews.

A data set of the health and socioeconomic factors that affect the elderly in Matlab, a region of rural Bangladesh. The survey captures measurements and statistics such as adult survival, health status, health care utilization, resource flows between generations and the impact of community services and infrastructure on adult health care. Data was collected through surveys that touch on four topics: household and individual information; determinants of natural fertility; migration out of the community; and community and provider survey of healthcare and education infrastructure.