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 2024 survey, 64 percent of individuals from Switzerland assessed their healthcare quality received as very good or good, while only 12 percent of Hungarian respondents rated the healthcare quality they have access to as good or very good.

The healthcare ranking reflects the quality of health care and access to health services in different countries. The assessment includes various factors such as life expectancy, access to medical services, healthcare funding, and technologies.

In 2024, roughly***** of individuals worldwide stated the quality of the healthcare they had access to in their country was good. The highest quality rating were given by people from Malaysia, Switzerland, and the Netherlands, while individuals in Hungary, Poland, and Peru rated their country's healthcare quality most poorly. This statistic presents the percentage of adults in select countries worldwide who agreed that the quality of the healthcare they had access to in their country was good or poor as of 2024.

In 2024, ** percent of adults worldwide agreed that many people in their country could not afford good healthcare. Individuals in Brazil were most likely to agree with this statement "Many people in my country cannot afford good healthcare.", while the least share of individuals agreed in Sweden. The results generally reflect the wealth of a nation, with people from wealthier countries tending to agree that good healthcare is affordable. The biggest exception being the U.S. where over ********* of U.S. respondents agreed that good health care is unaffordable to many despite being one of the richest country in the world. This statistic shows the percentage of adults in select countries worldwide who agreed that many people in their country could not afford good healthcare as of 2024.

According to a survey conducted in a selection of Latin American countries in 2024, Argentina was by far the country with the highest share of satisfied health patients, with ** percent of respondents assessing healthcare quality as good or very good, whereas only ** percent of respondents in Peru claimed to receive good healthcare. Hospitals in Latin America Hospital Israelita Albert Einstein in São Paulo, Brazil was considered the hospital with the highest care quality in Latin America in 2022. The first three leading hospitals in hosting patients were also located in Brazil, ranking high along other healthcare facilities in Argentina, Colombia and Chile. In 2024, Brazil was the country with the highest number of hospitals in the region, with approximately ***** establishments, followed by Mexico and Colombia. Hospital equipment in Latin America As of 2023, more than ** percent of hospitals in Latin America were equipped with electrocardiogram (EKG) machines. That year, ultrasound machines could be found in ** percent of hospitals, while a fourth of these establishments in the region had computed tomography (CT) scanners. In that year, Brazil had the most ultrasound machines installed in hospitals in Latin America, with over ******, followed by Mexico and Argentina.

BackgroundIt is increasingly apparent that access to healthcare without adequate quality of care is insufficient to improve population health outcomes. We assess whether the most commonly measured attribute of health facilities in low- and middle-income countries (LMICs)—the structural inputs to care—predicts the clinical quality of care provided to patients.Methods and findingsService Provision Assessments are nationally representative health facility surveys conducted by the Demographic and Health Survey Program with support from the US Agency for International Development. These surveys assess health system capacity in LMICs. We drew data from assessments conducted in 8 countries between 2007 and 2015: Haiti, Kenya, Malawi, Namibia, Rwanda, Senegal, Tanzania, and Uganda. The surveys included an audit of facility infrastructure and direct observation of family planning, antenatal care (ANC), sick-child care, and (in 2 countries) labor and delivery. To measure structural inputs, we constructed indices that measured World Health Organization-recommended amenities, equipment, and medications in each service. For clinical quality, we used data from direct observations of care to calculate providers’ adherence to evidence-based care guidelines. We assessed the correlation between these metrics and used spline models to test for the presence of a minimum input threshold associated with good clinical quality. Inclusion criteria were met by 32,531 observations of care in 4,354 facilities. Facilities demonstrated moderate levels of infrastructure, ranging from 0.63 of 1 in sick-child care to 0.75 of 1 for family planning on average. Adherence to evidence-based guidelines was low, with an average of 37% adherence in sick-child care, 46% in family planning, 60% in labor and delivery, and 61% in ANC. Correlation between infrastructure and evidence-based care was low (median 0.20, range from −0.03 for family planning in Senegal to 0.40 for ANC in Tanzania). Facilities with similar infrastructure scores delivered care of widely varying quality in each service. We did not detect a minimum level of infrastructure that was reliably associated with higher quality of care delivered in any service. These findings rely on cross-sectional data, preventing assessment of relationships between structural inputs and clinical quality over time; measurement error may attenuate the estimated associations.ConclusionInputs to care are poorly correlated with provision of evidence-based care in these 4 clinical services. Healthcare workers in well-equipped facilities often provided poor care and vice versa. While it is important to have strong infrastructure, it should not be used as a measure of quality. Insight into health system quality requires measurement of processes and outcomes of care.

IntroductionPrivate sector healthcare delivery in low- and middle-income countries is sometimes argued to be more efficient, accountable, and sustainable than public sector delivery. Conversely, the public sector is often regarded as providing more equitable and evidence-based care. We performed a systematic review of research studies investigating the performance of private and public sector delivery in low- and middle-income countries. Methods and FindingsPeer-reviewed studies including case studies, meta-analyses, reviews, and case-control analyses, as well as reports published by non-governmental organizations and international agencies, were systematically collected through large database searches, filtered through methodological inclusion criteria, and organized into six World Health Organization health system themes: accessibility and responsiveness; quality; outcomes; accountability, transparency, and regulation; fairness and equity; and efficiency. Of 1,178 potentially relevant unique citations, data were obtained from 102 articles describing studies conducted in low- and middle-income countries. Comparative cohort and cross-sectional studies suggested that providers in the private sector more frequently violated medical standards of practice and had poorer patient outcomes, but had greater reported timeliness and hospitality to patients. Reported efficiency tended to be lower in the private than in the public sector, resulting in part from perverse incentives for unnecessary testing and treatment. Public sector services experienced more limited availability of equipment, medications, and trained healthcare workers. When the definition of “private sector” included unlicensed and uncertified providers such as drug shop owners, most patients appeared to access care in the private sector; however, when unlicensed healthcare providers were excluded from the analysis, the majority of people accessed public sector care. “Competitive dynamics” for funding appeared between the two sectors, such that public funds and personnel were redirected to private sector development, followed by reductions in public sector service budgets and staff. ConclusionsStudies evaluated in this systematic review do not support the claim that the private sector is usually more efficient, accountable, or medically effective than the public sector; however, the public sector appears frequently to lack timeliness and hospitality towards patients. Please see later in the article for the Editors' Summary

In 2023, the health care system in Finland ranked first with a care index score of ****, followed by Belgium and Japan. Care systems index score is measured using multiple indicators from various public databases, it evaluates the capacity of a health system to treat and cure diseases and illnesses, once it is detected in the population This statistic shows the care systems ranking of countries worldwide in 2023, by their index score.

This spreadsheet provides the list of indicators related to the assessment of the quality of child healthcare collected from two type of sources: open-access international databases and national experts. It has been adopted to the Paper 'Quality of child healthcare in European countries: common measures across international databases and national agencies'.

This data package contains a wide spectrum of internationally comparable indicators that cover population demographics and population health status (including natality, mortality, quality of life and morbidity) and major determinants of health like healthcare system and services and behavioral health risk factors. It must be mentioned that OECD available data cover predominantly two major areas: population health status and healthcare services (resources and utilization).

ABSTRACT Objective: To describe the evaluation of patients that participated in the National Program for Improving the Access and Quality in Primary Health Care (Programa Nacional de Melhoria do Acesso e da Qualidade na Atenção Básica) for the comprehensive healthcare, the bond and the coordination of care in the country's macro-regions. Method: A descriptive, transversal study, from interviews with 65,391 patients of Primary Health Care, in 3,944 municipalities regarding the use of health services. Results: The professionals seek to solve the patients' problems in their unit (73.1%) but focused mainly on the scope of the appointment (65.6%) and offering care away from the population's reality (69.4%). Difficulties in the rescue of clinical history were referred (50.3%) and in the care performed in other health services (29.2%). Conclusion: The comprehensive health care, the bond and the coordination of care remain challenges to the Primary Health Care in the country, requiring reflections on the implementation of national policies, especially considering the regional diversities in Brazil.

BackgroundHigh satisfaction with healthcare is common in low- and middle-income countries (LMICs), despite widespread quality deficits. This may be due to low expectations because people lack knowledge about what constitutes good quality or are resigned about the quality of available services.Methods and findingsWe fielded an internet survey in Argentina, China, Ghana, India, Indonesia, Kenya, Lebanon, Mexico, Morocco, Nigeria, Senegal, and South Africa in 2017 (N = 17,996). It included vignettes describing poor-quality services—inadequate technical or interpersonal care—for 2 conditions. After applying population weights, most of our respondents lived in urban areas (59%), had finished primary school (55%), and were under the age of 50 (75%). Just over half were men (51%), and the vast majority reported that they were in good health (73%). Over half (53%) of our study population rated the quality of vignettes describing poor-quality services as good or better. We used multilevel logistic regression and found that good ratings were associated with less education (no formal schooling versus university education; adjusted odds ratio [AOR] 2.22, 95% CI 1.90–2.59, P < 0.001), better self-reported health (excellent versus poor health; AOR 5.19, 95% CI 4.33–6.21, P < 0.001), history of discrimination in healthcare (AOR 1.47, 95% CI 1.36–1.57, P < 0.001), and male gender (AOR 1.32, 95% CI 1.23–1.41, P < 0.001). The survey did not reach nonusers of the internet thus only representing the internet-using population.ConclusionsMajorities of the internet-using public in 12 LMICs have low expectations of healthcare quality as evidenced by high ratings given to poor-quality care. Low expectations of health services likely dampen demand for quality, reduce pressure on systems to deliver quality care, and inflate satisfaction ratings. Policies and interventions to raise people’s expectations of the quality of healthcare they receive should be considered in health system quality reforms.

Abstract

The Service Delivery Indicators (SDI) are a set of health and education indicators that examine the effort and ability of staff and the availability of key inputs and resources that contribute to a functioning school or health facility. The indicators are standardized, allowing comparison between and within countries over time.

The Health SDIs include healthcare provider effort, knowledge and ability, and the availability of key inputs (for example, basic equipment, medicines and infrastructure, such as toilets and electricity). The indicators provide a snapshot of the health facility and assess the availability of key resources for providing high quality care.

The Sierra Leone SDI Health survey team visited a sample of 536 health facilities across Sierra Leone between January and April 2018. The survey team collected rosters covering 5,055 workers for absenteeism and assessed 829 health workers for competence using patient case simulations.

Geographic coverage

National

Analysis unit

Health facilities and healthcare providers

Universe

All health facilities providing primary-level care

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling strategy for SDI surveys is designed towards attaining indicators that are accurate and representative at the national level, as this allows for proper cross-country (i.e. international benchmarking) and across time comparisons, when applicable. In addition, other levels of representativeness are sought to allow for further disaggregation (rural/urban areas, public/private facilities, subregions, etc.) during the analysis stage.

The sampling strategy for SDI surveys follows a multistage sampling approach. The main units of analysis are facilities (schools and health centers) and providers (health and education workers: teachers, doctors, nurses, facility managers, etc.). The multi-stage sampling approach makes sampling procedures more practical by dividing the selection of large populations of sampling units in a step-by-step fashion. After defining the sampling frame and categorizing it by stratum, a first stage selection of sampling units is carried out independently within each stratum. Often, the primary sampling units (PSU) for this stage are cluster locations (e.g. districts, communities, counties, neighborhoods, etc.) which are randomly drawn within each stratum with a probability proportional to the size (PPS) of the cluster (measured by the location’s number of facilities, providers or pupils). Once locations are selected, a second stage takes place by randomly selecting facilities within location (either with equal probability or with PPS) as secondary sampling units. At a third stage, a fixed number of health and education workers and pupils are randomly selected within facilities to provide information for the different questionnaire modules.

Detailed information about the specific sampling process is available in the associated SDI Country Report included as part of the documentation that accompany these datasets.

Mode of data collection

Face-to-face [f2f]

Research instrument

The SDI Health Survey Questionnaire consists of four modules:

Module 1: General Information - Administered to the health facility manager to collect information on equipment, medicines, infrastructure and other facets of the health facility.

Module 2: Provider Absence - A roster of healthcare providers is collected and absence measured.

Module 3: Clinical Vignettes – A selection of providers are given clinical vignettes to measure knowledge of common medical conditions.

Module 4: Facility finances – Information on facility revenue and expenditures is collected from the health facility manager.

Weights: Weights for facilities, absentee-related analyses and clinical vignette analyses.

Cleaning operations

Quality control was performed in Stata.

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.

Abstract copyright UK Data Service and data collection copyright owner. The Organisation for Economic Co-operation and Development (OECD) Health Statistics offers the most comprehensive source of comparable statistics on health and health systems across OECD countries. It is an essential tool for health researchers and policy advisors in governments, the private sector and the academic community, to carry out comparative analyses and draw lessons from international comparisons of diverse health care systems. Within UKDS.Stat the data are presented in the following databases: Health status This datasets presents internationally comparable statistics on morbidity and mortality with variables such as life expectancy, causes of mortality, maternal and infant mortality, potential years of life lost, perceived health status, infant health, dental health, communicable diseases, cancer, injuries, absence from work due to illness. The annual data begins in 2000. Non-medical determinants of health This dataset examines the non-medical determinants of health by comparing food, alcohol, tobacco consumption and body weight amongst countries. The data are expressed in different measures such as calories, grammes, kilo, gender, population. The data begins in 1960. Healthcare resources This dataset includes comparative tables analyzing various health care resources such as total health and social employment, physicians by age, gender, categories, midwives, nurses, caring personnel, personal care workers, dentists, pharmacists, physiotherapists, hospital employment, graduates, remuneration of health professionals, hospitals, hospital beds, medical technology with their respective subsets. The statistics are expressed in different units of measure such as number of persons, salaried, self-employed, per population. The annual data begins in 1960. Healthcare utilisation This dataset includes statistics comparing different countries’ level of health care utilisation in terms of prevention, immunisation, screening, diagnostics exams, consultations, in-patient utilisation, average length of stay, diagnostic categories, acute care, in-patient care, discharge rates, transplants, dialyses, ICD-9-CM. The data is comparable with respect to units of measures such as days, percentages, population, number per capita, procedures, and available beds. Health Care Quality Indicators This dataset includes comparative tables analyzing various health care quality indicators such as cancer care, care for acute exacerbation of chronic conditions, care for chronic conditions and care for mental disorders. The annual data begins in 1995. Pharmaceutical market This dataset focuses on the pharmaceutical market comparing countries in terms of pharmaceutical consumption, drugs, pharmaceutical sales, pharmaceutical market, revenues, statistics. The annual data begins in 1960. Long-term care resources and utilisation This dataset provides statistics comparing long-term care resources and utilisation by country in terms of workers, beds in nursing and residential care facilities and care recipients. In this table data is expressed in different measures such as gender, age and population. The annual data begins in 1960. Health expenditure and financing This dataset compares countries in terms of their current and total expenditures on health by comparing how they allocate their budget with respect to different health care functions while looking at different financing agents and providers. The data covers the years starting from 1960 extending until 2010. The countries covered are Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, and United States. Social protection This dataset introduces the different health care coverage systems such as the government/social health insurance and private health insurance. The statistics are expressed in percentage of the population covered or number of persons. The annual data begins in 1960. Demographic references This dataset provides statistics regarding general demographic references in terms of population, age structure, gender, but also in term of labour force. The annual data begins in 1960. Economic references This dataset presents main economic indicators such as GDP and Purchasing power parities (PPP) and compares countries in terms of those macroeconomic references as well as currency rates, average annual wages. The annual data begins in 1960. These data were first provided by the UK Data Service in November 2014.

This database contains the whole amount of indicators retrieved by systematic search as well as the total of indicators that were analyzed, including information on applied appraisal criteria.

Abstract

The Service Delivery Indicators (SDI) are a set of health and education indicators that examine the effort and ability of staff and the availability of key inputs and resources that contribute to a functioning school or health facility. The indicators are standardized, allowing comparison between and within countries over time.

The Health SDIs include healthcare provider effort, knowledge and ability, and the availability of key inputs (for example, basic equipment, medicines and infrastructure, such as toilets and electricity). The indicators provide a snapshot of the health facility and assess the availability of key resources for providing high quality care.

The Uganda SDI Health survey team visited a sample of 394 health facilities across Uganda between June and October 2013. The survey team collected rosters covering 2,347 workers for absenteeism and assessed 733 health workers for competence using patient case simulations.

Geographic coverage

National

Analysis unit

Health facilities and healthcare providers

Universe

All health facilities providing primary-level care.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling strategy for SDI surveys is designed towards attaining indicators that are accurate and representative at the national level, as this allows for proper cross-country (i.e. international benchmarking) and across time comparisons, when applicable. In addition, other levels of representativeness are sought to allow for further disaggregation (rural/urban areas, public/private facilities, subregions, etc.) during the analysis stage.

The sampling strategy for SDI surveys follows a multistage sampling approach. The main units of analysis are facilities (schools and health centers) and providers (health and education workers: teachers, doctors, nurses, facility managers, etc.). The multi-stage sampling approach makes sampling procedures more practical by dividing the selection of large populations of sampling units in a step-by-step fashion. After defining the sampling frame and categorizing it by stratum, a first stage selection of sampling units is carried out independently within each stratum. Often, the primary sampling units (PSU) for this stage are cluster locations (e.g. districts, communities, counties, neighborhoods, etc.) which are randomly drawn within each stratum with a probability proportional to the size (PPS) of the cluster (measured by the location’s number of facilities, providers or pupils). Once locations are selected, a second stage takes place by randomly selecting facilities within location (either with equal probability or with PPS) as secondary sampling units. At a third stage, a fixed number of health and education workers and pupils are randomly selected within facilities to provide information for the different questionnaire modules.

Detailed information about the specific sampling process is available in the associated SDI Country Report included as part of the documentation that accompany these datasets.

Mode of data collection

Face-to-face [f2f]

Research instrument

The SDI Health Survey Questionnaire consists of four modules and weights:

Module 1: General Information - Administered to the health facility manager to collect information on equipment, medicines, infrastructure and other facets of the health facility.

Module 2: Provider Absence - A roster of healthcare providers is collected and absence measured.

Module 3: Clinical Vignettes – A selection of providers are given clinical vignettes to measure knowledge of common medical conditions.

Module 4: Facility finances – Information on facility revenue and expenditures is collected from the health facility manager.

Weights: Weights for facilities, absentee-related analyses and clinical vignette analyses.

Cleaning operations

Quality control was performed in Stata.

Effective supportive supervision of healthcare services is crucial for improving and maintaining quality of care. However, this process can be challenging in an environment with chronic shortage of qualified human resources, overburdened healthcare providers, multiple roles of district managers, weak supply chains, high donor fragmentation and inefficient allocation of limited financial resources. Operating in this environment, we systematically evaluated an approach developed in Tanzania to strengthen the implementation of routine supportive supervision of primary healthcare providers. The approach included a systematic quality assessment at health facilities using an electronic tool and subsequent result dissemination at council level. Mixed methods were used to compare the new supportive supervision approach with routine supportive supervision. Qualitative data was collected through in-depth interviews in three councils. Observational data and informal communication as well as secondary data complemented the data set. Additionally, an economic costing analysis was carried out in the same councils. Compared to routine supportive supervision, the new approach increased healthcare providers’ knowledge and skills, as well as quality of data collected and acceptance of supportive supervision amongst stakeholders involved. It also ensured better availability of evidence for follow-up actions, including budgeting and planning, and higher stakeholder motivation and ownership of subsequent quality improvement measures. The new approach reduced time and cost spent during supportive supervision. This increased feasibility of supportive supervision and hence the likelihood of its implementation. Thus, the results presented together with previous findings suggested that if used as the standard approach for routine supportive supervision the new approach offers a suitable option to make supportive supervision more efficient and effective and therewith more sustainable. Moreover, the new approach also provides informed guidance to overcome several problems of supportive supervision and healthcare quality assessments in low- and middle income countries.

In the light of the importance of patient safety, this Eurobarometer survey has been conducted with the main objective of exploring Europeans' perceptions regarding patient safety and their attitudes toward the quality of healthcare in their country and cross-border. Since patient safety is such a serious concern, the Council of the European Union recently adopted the recommendation on patient safety, including the prevention and control of healthcare-associated infections (hereafter referred to as the Council recommendations). In brief, these recommendations cover measures to help prevent and reduce the occurrence of adverse events in healthcare, such as: • Greater reporting of patient safety events. It is recommended that more comprehensive reporting on adverse events take place, in a blame-free manner. This will help monitor and control patient safety, but also provide data on the effectiveness of implemented measures. • Education and training of healthcare workers, focusing on patient safety. Patient safety should be embedded in the education and training of all healthcare workers, including on-the-job training and the development of core competencies in patient safety. • Greater awareness of patient safety amongst patients. Patients themselves need to be aware of the authorities responsible for patient safety, the patient safety measures and standards which are in place, and available complaints procedures. • Standardisation of patient safety measures, definitions and terminology. The Member States are at different levels of development and implementation of patient safety strategies. It is recommended that common terminology, as well as patient safety standards and best practices be developed and shared amongst Member States.

The results by volumes are distributed as follows:

• Volume A: Countries
• Volume AA: Groups of countries
• Volume A' (AP): Trends
• Volume AA' (AAP): Trends of groups of countries
• Volume B: EU/socio-demographics
• Volume B' (BP) : Trends of EU/ socio-demographics
• Volume C: Country/socio-demographics ---- Researchers may also contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer