In 2023, Norway ranked first with a health index score of 83, followed by Iceland and Sweden. The health index score is calculated by evaluating various indicators that assess the health of the population, and access to the services required to sustain good health, including health outcomes, health systems, sickness and risk factors, and mortality rates. The statistic shows the health and health systems ranking of European countries in 2023, by their health index score.

In 2023, Singapore dominated the ranking of the world's health and health systems, followed by Japan and South Korea. The health index score is calculated by evaluating various indicators that assess the health of the population, and access to the services required to sustain good health, including health outcomes, health systems, sickness and risk factors, and mortality rates. The health and health system index score of the top ten countries with the best healthcare system in the world ranged between 82 and 86.9, measured on a scale of zero to 100.

Global Health Security Index  Numerous health and health system indexes have been developed to assess various attributes and aspects of a nation's healthcare system. One such measure is the Global Health Security (GHS) index. This index evaluates the ability of 195 nations to identify, assess, and mitigate biological hazards in addition to political and socioeconomic concerns, the quality of their healthcare systems, and their compliance with international finance and standards. In 2021, the United States was ranked at the top of the GHS index, but due to multiple reasons, the U.S. government failed to effectively manage the COVID-19 pandemic. The GHS Index evaluates capability and identifies preparation gaps; nevertheless, it cannot predict a nation's resource allocation in case of a public health emergency.

Universal Health Coverage Index  Another health index that is used globally by the members of the United Nations (UN) is the universal health care (UHC) service coverage index. The UHC index monitors the country's progress related to the sustainable developmental goal (SDG) number three. The UHC service coverage index tracks 14 indicators related to reproductive, maternal, newborn, and child health, infectious diseases, non-communicable diseases, service capacity, and access to care. The main target of universal health coverage is to ensure that no one is denied access to essential medical services due to financial hardships. In 2021, the UHC index scores ranged from as low as 21 to a high score of 91 across 194 countries. 

According to a 2021 health care systems ranking among selected high-income countries, the United States came last in the overall ranking of its health care system performance. The overall ranking was based on five performance categories, including access to care, care process, administrative efficiency, equity, and health care outcomes. For the category administrative efficiency, which measures the amount of paperwork for providers and patients in the health system, the U.S. was ranked last, while Norway took first place. This could be because the health system in the U.S. is a multi-payer system, while Norway has a single-payer system, which most likely simplifies documentation and billing tasks. This statistic present the health care administrative efficiency rankings of the United States' health care system compared to ten other high-income countries in 2021.

This horizontal bar chart displays health expenditure (% of GDP) by continent using the aggregation average, weighted by gdp in Norway. The data is filtered where the date is 2021. The data is about countries per year.

Norway Index: Oslo Bors Stock Exchange: Health Care: Health Care Equipments and Services data was reported at 959.370 29Dec1995=100 in Sep 2018. This records an increase from the previous number of 944.650 29Dec1995=100 for Aug 2018. Norway Index: Oslo Bors Stock Exchange: Health Care: Health Care Equipments and Services data is updated monthly, averaging 288.630 29Dec1995=100 from Aug 2001 (Median) to Sep 2018, with 206 observations. The data reached an all-time high of 979.920 29Dec1995=100 in Jul 2018 and a record low of 98.250 29Dec1995=100 in Mar 2003. Norway Index: Oslo Bors Stock Exchange: Health Care: Health Care Equipments and Services data remains active status in CEIC and is reported by Oslo Stock Exchange. The data is categorized under Global Database’s Norway – Table NO.Z001: Oslo Stock Exchange: Index.

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

Norway NO: Domestic Private Health Expenditure Per Capita: Current Price data was reported at 0.001 USD mn in 2015. This records a decrease from the previous number of 0.001 USD mn for 2014. Norway NO: Domestic Private Health Expenditure Per Capita: Current Price data is updated yearly, averaging 0.001 USD mn from Dec 2003 (Median) to 2015, with 13 observations. The data reached an all-time high of 0.001 USD mn in 2011 and a record low of 0.001 USD mn in 2003. Norway NO: Domestic Private 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 Norway – Table NO.World Bank: Health Statistics. Current private expenditures on health per capita expressed in current US dollars. Domestic private sources include funds from households, corporations and non-profit organizations. Such expenditures can be either prepaid to voluntary health insurance or paid directly to healthcare providers.; ; World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database).; Weighted Average;

Norway NO: Current Health Expenditure: % of GDP data was reported at 9.976 % in 2015. This records an increase from the previous number of 9.346 % for 2014. Norway NO: Current Health Expenditure: % of GDP data is updated yearly, averaging 8.809 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 9.976 % in 2015 and a record low of 7.709 % in 2000. Norway NO: Current Health Expenditure: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Norway – Table NO.World Bank: Health Statistics. 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.; ; World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database).; Weighted Average;

As of 2023, smartphones were the most common device for communicating with healthcare professionals in the Nordic countries, with the highest share of usage recorded in Norway at 78 percent. The second most utilized device in communication with healthcare professionals are computers, used in Denmark by around 70 percent of respondents.

In 2023, the United States had the highest per capita health expenditure among OECD countries. At that time, per capita health expenditure in the U.S. amounted over ****** U.S. dollars, significantly higher than in Switzerland, the country with the second-highest per capita health expenditure. Norway, Germany and Austria are also within the top five countries with the highest per capita health expenditure. The United States also spent the highest share of it’s gross domestic product on health care, with **** percent of its GDP spent on health care services. Health Expenditure in the U.S. The United States is the highest spending country worldwide when it comes to health care. In 2022, total health expenditure in the U.S. exceeded **** trillion dollars. Expenditure as a percentage of GDP is projected to increase to approximately ** percent by the year 2031. Distribution of Health Expenditure in the U.S. Health expenditure in the United States is spread out across multiple categories such as nursing home facilities, home health care, and prescription drugs. As of 2022, the majority of health expenditure in the United States was spent on hospital care, accounting for a bit less than *** third of all health spending. Hospital care was followed by spending on physician and clinical services which accounted for ** percent of overall health expenditure.

In 2018, 99 percent of survey respondents in Norway had trust in doctors and nurses in their country, the highest share of any European country. Four other European countries - Iceland, Finland, Denmark and Spain - had 97 percent of respondents stating they had confidence in their doctors and nurses.

Norway NO: Lifetime Risk of Maternal Death: 1 in: Rate Varies by Country data was reported at 11,500.000 NA in 2015. This records an increase from the previous number of 11,300.000 NA for 2014. Norway NO: Lifetime Risk of Maternal Death: 1 in: Rate Varies by Country data is updated yearly, averaging 8,000.000 NA from Dec 1990 (Median) to 2015, with 26 observations. The data reached an all-time high of 11,500.000 NA in 2015 and a record low of 7,300.000 NA in 1991. Norway NO: Lifetime Risk of Maternal Death: 1 in: Rate Varies by Country data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Norway – Table NO.World Bank: Health Statistics. Life time risk of maternal death is the probability that a 15-year-old female will die eventually from a maternal cause assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future, taking into account competing causes of death.; ; WHO, UNICEF, UNFPA, World Bank Group, and the United Nations Population Division. Trends in Maternal Mortality: 1990 to 2015. Geneva, World Health Organization, 2015; Weighted average;

BackgroundThe COVID-19 pandemic is commonly believed to have increased common mental disorders (CMD, i.e., depression and anxiety), either directly due to COVID-19 contractions (death of near ones or residual conditions), or indirectly by increasing stress, economic uncertainty, and disruptions in daily life resulting from containment measure. Whereas studies reporting on initial changes in self-reported data frequently have reported increases in CMD, pandemic related changes in CMD related to primary care utilization are less well known. Analyzing time series of routinely and continuously sampled primary healthcare data from Sweden, Norway, Netherlands, and Latvia, we aimed to characterize the impact of the pandemic on CMD recorded prevalence in primary care. Furthermore, by relating these changes to country specific time-trajectories of two classes of containment measures, we evaluated the differential impact of containment strategies on CMD rates. Specifically, we wanted to test whether school restrictions would preferentially affect age groups corresponding to those of school children or their parents.MethodsFor the four investigated countries, we collected time-series of monthly counts of unique CMD patients in primary healthcare from the year 2015 (or 2017) until 2021. Using pre-pandemic timepoints to train seasonal Auto Regressive Integrated Moving Average (ARIMA) models, we predicted healthcare utilization during the pandemic. Discrepancies between observed and expected time series were quantified to infer pandemic related changes. To evaluate the effects of COVID-19 measures on CMD related primary care utilization, the predicted time series were related to country specific time series of levels of social distancing and school restrictions.ResultsIn all countries except Latvia there was an initial (April 2020) decrease in CMD care prevalence, where largest drops were found in Sweden (Prevalence Ratio, PR = 0.85; 95% CI 0.81–0.90), followed by Netherlands (0.86; 95% CI 0.76–1.02) and Norway (0.90; 95% CI 0.83–0.98). Latvia on the other hand experienced increased rates (1.25; 95% CI 1.08–1.49). Whereas PRs in Norway and Netherlands normalized during the latter half of 2020, PRs stayed low in Sweden and elevated in Latvia. The overall changes in PR during the pandemic year 2020 was significantly changed only for Sweden (0.91; 95% CI 0.90–0.93) and Latvia (1.20; 95% CI 1.14–1.26). Overall, the relationship between containment measures and CMD care prevalence were weak and non-significant. In particular, we could not observe any relationship of school restriction to CMD care prevalence for the age groups best corresponding to school children or their parents.ConclusionCommon mental disorders prevalence in primary care decreased during the initial phase of the COVID-19 pandemic in all countries except from Latvia, but normalized in Norway and Netherlands by the latter half of 2020. The onset of the pandemic and the containment strategies were highly correlated within each country, limiting strong conclusions on whether restriction policy had any effects on mental health. Specifically, we found no evidence of associations between school restrictions and CMD care prevalence. Overall, current results lend no support to the common belief that the pandemic severely impacted the mental health of the general population as indicated by healthcare utilization, apart from in Latvia. However, since healthcare utilization is affected by multiple factors in addition to actual need, future studies should combine complementary types of data to better understand the mental health impacts of the pandemic.

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

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BackgroundDecades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic.MethodsWe used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy.FindingsAll countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy across all countries (overall mean –0·18 years [95% UI –0·22 to –0·13]), with all countries having an absolute fall in life expectancy except for Ireland, Iceland, Sweden, Norway, and Denmark, which showed marginal improvement in life expectancy, and Belgium, which showed no change in life expectancy. Across countries, the causes of death responsible for the largest improvements in life expectancy from 1990 to 2011 were cardiovascular diseases and neoplasms. Deaths from cardiovascular diseases were the primary driver of reductions in life expectancy improvements during 2011–19, and deaths from respiratory infections and other COVID-19 pandemic-related outcomes were responsible for the decreases in life expectancy during 2019–21. Deaths from cardiovascular diseases and neoplasms in 2019 were attributable to high systolic blood pressure, dietary risks, tobacco smoke, high LDL cholesterol, high BMI, occupational risks, high alcohol use, and other risks including low physical activity. Exposure to these major risk factors differed by country, with trends of increasing exposure to high BMI and decreasing exposure to tobacco smoke observed in all countries during 1990–2021.InterpretationThe countries that best maintained improvements in life expectancy after 2011 (Norway, Iceland, Belgium, Denmark, and Sweden) did so through better maintenance of reductions in mortality from cardiovascular diseases and neoplasms, underpinned by decreased exposures to major risks, possibly mitigated by government policies. The continued improvements in life expectancy in five countries during 2019–21 indicate that these countries were better prepared to withstand the COVID-19 pandemic. By contrast, countries with the greatest slowdown in life expectancy improvements after 2011 went on to have some of the largest decreases in life expectancy in 2019–21. These findings suggest that government policies that improve population health also build resilience to future shocks. Such policies include reducing population exposure to major upstream risks for cardiovascular diseases and neoplasms, such as harmful diets and low physical activity, tackling the commercial determinants of poor health, and ensuring access to affordable health services.

In 2021, the number of individuals employed in the health and social care sector in Denmark increased by 24 thousand employees (+4.52 percent) since 2020. With 555 thousand employees, the number of individuals employed in the health sector thereby reached its highest value in the observed period. Find more key insights for the number of individuals employed in the health and social care sector in countries like Finland, Norway, and Sweden.

NO: External Health Expenditure: % of Current Health Expenditure data was reported at 0.000 % in 2015. This stayed constant from the previous number of 0.000 % for 2014. NO: External Health Expenditure: % of Current Health Expenditure data is updated yearly, averaging 0.000 % from Dec 2003 (Median) to 2015, with 13 observations. The data reached an all-time high of 0.054 % in 2008 and a record low of 0.000 % in 2015. NO: 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 Norway – Table NO.World Bank: 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).; Weighted Average;

This document outlines the protocol for a scoping review that aims to map existing research on task-shifting in eye health services in high-income countries. Task-shifting involves reallocating specific tasks from highly specialized professionals to less specialized workers, patients, caregivers, or technology. The review will explore the types of tasks shifted, methods of task-shifting, its effects, and barriers and facilitators to implementation. It follows the JBI methodology and includes studies from 2000 onward in English, German, Dutch, and Scandinavian languages. The findings will support evidence-informed strategies to enhance service delivery and address workforce challenges in vision and eye care

BackgroundMost of the evidence on antimicrobial stewardship programmes (ASP) to help sustain the effectiveness of antimicrobials is generated in high income countries. We report a study investigating implementation of ASP in secondary care across low-, middle- and high-income countries. The objective of this study was to map the key contextual, including cultural, drivers of the development and implementation of ASP across different resource settings.Materials and methodsHealthcare professionals responsible for implementing ASP in hospitals in England, France, Norway, India, and Burkina Faso were invited to participate in face-to face interviews. Field notes from observations, documentary evidence, and interview transcripts were analysed using grounded theory approach. The key emerging categories were analysed iteratively using constant comparison, initial coding, going back the field for further data collection, and focused coding. Theoretical sampling was applied until the categories were saturated. Cross-validation and triangulation of the findings were achieved through the multiple data sources.Results54 participants from 24 hospitals (England 9 participants/4 hospitals; Norway 13 participants/4 hospitals; France 9 participants/7 hospitals; India 13 participants/ 7 hospitals; Burkina Faso 8 participants/2 hospitals) were interviewed. Across Norway, France and England there was consistency in ASP structures. In India and Burkina Faso there were country level heterogeneity in ASP. State support for ASP was perceived as essential in countries where it is lacking (India, Burkina Faso), and where it was present, it was perceived as a barrier (England, France). Professional boundaries are one of the key cultural determinants dictating involvement in initiatives with doctors recognised as leaders in ASP. Nurse and pharmacist involvement was limited to England. The surgical specialty was identified as most difficult to engage with in each country. Despite challenges, one hospital in India provided the best example of interdisciplinary ASP, championed through organisational leadership.ConclusionsASP initiatives in this study were restricted by professional boundaries and hierarchies, with lack of engagement with the wider healthcare workforce. There needs to be promotion of interdisciplinary team work including pharmacists and nurses, depending on the available healthcare workforce in different countries, in ASP. The surgical pathway remains a hard to reach, but critical target for ASP globally. There is a need to develop contextually driven ASP targeting the surgical pathway in different resource settings.

Norway NO: External Health Expenditure Per Capita: Current PPP data was reported at 0.000 Intl $ mn in 2015. This stayed constant from the previous number of 0.000 Intl $ mn for 2014. Norway NO: External Health Expenditure Per Capita: Current PPP data is updated yearly, averaging 0.000 Intl $ mn from Dec 2003 (Median) to 2015, with 13 observations. The data reached an all-time high of 0.000 Intl $ mn in 2008 and a record low of 0.000 Intl $ mn in 2015. Norway NO: 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 Norway – Table NO.World Bank: Health Statistics. Current external expenditures on health per capita expressed in international dollars at purchasing power parity (PPP). 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).; Weighted Average;