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. 

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.

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.

According to a survey from *************, Taiwan was ranked as the best country for expat healthcare, followed by South Korea and Qatar. This statistic represents the ranking of top ten countries with best healthcare for expats worldwide in 2023.

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.

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 2025, South Africa had the highest health care index in Africa with a score of 63.8, followed by Kenya with 62 points. These scores, for both countries, are considered to be reasonably high. The health care index takes into account factors such as the overall quality of the health care system, health care professionals, equipment, staff, doctors, and cost.

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.

The average for 2020 based on 36 countries was 4.44 hospital beds. The highest value was in South Korea: 12.65 hospital beds and the lowest value was in Mexico: 0.99 hospital beds. The indicator is available from 1960 to 2021. Below is a chart for all countries where data are available.

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

According to a study from 2024, ******* medical infrastructure and professionals were ranked as the best in the world, scoring **** out of a possible 100. Ranked as second and third were Italy and Iceland. This statistic represents the ranking of the top 20 countries with the best medical infrastructure and professionals worldwide in 2024, by index score.

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.

Context

In order to begin correlating global data based around infection rates, from the WHO data in the UNCOVER: Covid-19 challenge, found here, to quality of healthcare in a region, data relaying the availability of health care in nations around the globe is necessary as a first step to this analysis. Out of a general desire to provide this data to the data science community, and out of a desire to find ways to learn about, prepare for in whatever way possible, and beat, the COVID-19 pandemic of 2020, I'm making this data-set public for others to use, share, and study with.

Content

The data presented in the file below cover the following information... 1 set of Strings --> The country names 1 set of Integers --> The years in which the data were recorded (2010-2014). 6 sets of floats --> 6 columns of floats record the total density of health centers and hospitals (including provincial and specialized) to every 100,000 people within the country... thus generalizing the country's access to health care, and maintenance/creation of the health infrastructure needed to support the population.

Acknowledgements

Complete thanks for this data-set goes to the World Health Organization and the Global Health Observatory. This data can be found on the GHO's site, specifically here. In terms of the licensing, in order to underscore that this data is not mine, as well as ensure all steps are taken to make one's proper rights clear (and grant thanks for the data once again), the general data usage license agreement for the data-set used can be found here.

Inspiration

It is sadly true that this data on its own is unlikely to present any major answers. When combined with other datasets however, this may yield answers as to what factors of a countries existence may indicate its ability to maintain a large health infrastructure. In fact, determining how a country's finances, natural resource list (as just ideas), etc. relate to a country's ability to sustain a decent health infrastructure would be an extremely interesting question to answer. I hope you may find the data helpful in your endeavors!

Disclaimer: This is my first ever published data-set on Kaggle. While I've done my best to ensure it's fairly descriptive for any potential visitors, please do feel free to leave any comments you may have in the discussions section! I'm always open to finding ways to improve.

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.

The average for 2021 based on 181 countries was 7.21 percent. The highest value was in Afghanistan: 21.83 percent and the lowest value was in Brunei: 2.2 percent. The indicator is available from 2000 to 2022. Below is a chart for all countries where data are available.

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South Korea Number of Hospital was up 3.5% in 2019, compared to the previous year.

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.

BackgroundOne of the greatest obstacles facing efforts to address quality of care in low and middle income countries is the absence of relevant and reliable data. This article proposes a methodology for creating a single “Quality Index” (QI) representing quality of maternal and neonatal health care based upon data collected as part of the Demographic and Health Survey (DHS) program.MethodsUsing the 2012 Indonesian Demographic and Health Survey dataset, indicators of quality of care were identified based on the recommended guidelines outlined in the WHO Integrated Management of Pregnancy and Childbirth. Two sets of indicators were created; one set only including indicators available in the standard DHS questionnaire and the other including all indicators identified in the Indonesian dataset. For each indicator set composite indices were created using Principal Components Analysis and a modified form of Equal Weighting. These indices were tested for internal coherence and robustness, as well as their comparability with each other. Finally a single QI was chosen to explore the variation in index scores across a number of known equity markers in Indonesia including wealth, urban rural status and geographical region.ResultsThe process of creating quality indexes from standard DHS data was proven to be feasible, and initial results from Indonesia indicate particular disparities in the quality of care received by the poor as well as those living in outlying regions.ConclusionsThe QI represents an important step forward in efforts to understand, measure and improve quality of MNCH care in developing countries.

IntroductionIn confronting the sudden COVID-19 epidemic, China and other countries have been under great pressure to block virus transmission and reduce fatalities. Converting large-scale public venues into makeshift hospitals is a popular response. This addresses the outbreak and can maintain smooth operation of a country or region's healthcare system during a pandemic. However, large makeshift hospitals, such as the Shanghai New International Expo Center (SNIEC) makeshift hospital, which was one of the largest makeshift hospitals in the world, face two major problems: Effective and precise transfer of patients and heterogeneity of the medical care teams.MethodsTo solve these problems, this study presents the medical practices of the SNIEC makeshift hospital in Shanghai, China. The experiences include constructing two groups, developing a medical management protocol, implementing a multi-dimensional management mode to screen patients, transferring them effectively, and achieving homogeneous quality of medical care. To evaluate the medical practice performance of the SNIEC makeshift hospital, 41,941 infected patients were retrospectively reviewed from March 31 to May 23, 2022. Multivariate logistic regression method and a tree-augmented naive (TAN) Bayesian network mode were used.ResultsWe identified that the three most important variables were chronic disease, age, and type of cabin, with importance values of 0.63, 0.15, and 0.11, respectively. The constructed TAN Bayesian network model had good predictive values; the overall correct rates of the model-training dataset partition and test dataset partition were 99.19 and 99.05%, respectively, and the respective values for the area under the receiver operating characteristic curve were 0.939 and 0.957.ConclusionThe medical practice in the SNIEC makeshift hospital was implemented well, had good medical care performance, and could be copied worldwide as a practical intervention to fight the epidemic in China and other developing countries.