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 health care outcomes, which takes into account health outcomes most likely to be responsive to health care, the U.S. was ranked last, while Australia took first place. Outcomes such as infant mortality or preventable mortality were included. This statistic present the health care outcomes rankings of the United States' health care system compared to ten other high-income countries in 2021.

In 2023, Singapore ranked first with a health index score of ****, followed by Japan and South Korea. The health index measures the extent to which people are healthy and have access to the necessary services to maintain good health, including health outcomes, health systems, illness and risk factors, and mortality rates. The statistic shows the health and health systems ranking of countries worldwide in 2023, by their health 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.

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

The aim of the article is to compare health system outcomes in the BRICS countries, assess the trends of their changes in 2000−2017, and verify whether they are in any way correlated with the economic context. The indicators considered were: nominal and per capita current health expenditure, government health expenditure, gross domestic product (GDP) per capita, GDP growth, unemployment, inflation, and composition of GDP. The study covered five countries of the BRICS group over a period of 18 years. We decided to characterize countries covered with a dataset of selected indicators describing population health status, namely: life expectancy at birth, level of immunization, infant mortality rate, maternal mortality ratio, and tuberculosis case detection rate. We constructed a unified synthetic measure depicting the performance of individual health systems in terms of their outcomes with a single numerical value. Descriptive statistical analysis of quantitative traits consisted of the arithmetic mean (xsr), standard deviation (SD), and, where needed, the median. The normality of the distribution of variables was tested with the Shapiro–Wilk test. Spearman's rho and Kendall tau rank coefficients were used for correlation analysis between measures. The correlation analyses have been supplemented with factor analysis. We found that the best results in terms of health care system performance were recorded in Russia, China, and Brazil. India and South Africa are noticeably worse. However, the entire group performs visibly worse than the developed countries. The health system outcomes appeared to correlate on a statistically significant scale with health expenditures per capita, governments involvement in health expenditures, GDP per capita, and industry share in GDP; however, these correlations are relatively weak, with the highest strength in the case of government's involvement in health expenditures and GDP per capita. Due to weak correlation with economic background, other factors may play a role in determining health system outcomes in BRICS countries. More research should be recommended to find them and determine to what extent and how exactly they affect health system outcomes.

This dataset provides an extensive view of global population statistics and health metrics across various countries from 2014 to 2024. It combines population data with vital health-related indicators, making it a valuable resource for understanding trends in population growth and health outcomes worldwide. Researchers, data scientists, and policymakers can utilize this dataset to analyze correlations between population dynamics and health performance at a global scale.

Key Features: - Country: Name of the country. - Year: Year of the data (2014–2024). - Population: Total population for the respective year and country. - Country Code: ISO 3-letter country codes for easy identification. - Health Expenditure (health_exp): Percentage of GDP spent on healthcare. - Life Expectancy (life_expect): Average life expectancy at birth in years. - Maternal Mortality (maternal_mortality): Maternal deaths per 100,000 live births. - Infant Mortality (infant_mortality): Deaths of infants under 1 year per 1,000 live births. - Neonatal Mortality (neonatal_mortality): Deaths of newborns (0–28 days) per 1,000 live births. - Under-5 Mortality (under_5_mortality): Deaths of children under 5 years per 1,000 live births. - HIV Prevalence (prev_hiv): Percentage of the population living with HIV. - Tuberculosis Incidence (inci_tuberc): Estimated new and relapse TB cases per 100,000 people. - Undernourishment Prevalence (prev_undernourishment): Percentage of the population that is undernourished.

Use Cases: - Health Policy Analysis: Understand trends in healthcare expenditure and its relationship to health outcomes. - Global Health Research: Investigate global or regional disparities in health and nutrition. - Population Studies: Analyze population growth trends alongside health indicators. - Data Visualization: Build visual dashboards for storytelling and impactful data representation.

Exploratory analysis of the impact of corruption on health outcomes in 12 CEE countries during the period 2012-2020, using fixed effects panel data analysis. Variables were selected from various databases and combined into one dataset. Methodology: Our conceptual model seeks to ascertain any association between corruption and two core health system functions, improving health (incorporating access and quality) and fair financing. We capture access and quality using Eurostat data on avoidable mortality. We capture fair financing using data on out-of-pocket (OOP) expenses, sourced from the World Bank’s World Development Indicators. Given that there is no single pathway between corruption and health outcomes, we chose to explore two indicators which are reflective of a well-functioning and equitable healthcare system. OOP payments, which includes informal payments, are indicative of catastrophic spending and therefore may be on the pathway as such payments may limit access to essential and timely health services needed to retain good health. Similarly, avoidable mortality is indicative of a responsive and efficient healthcare system and may indicate the scope for health improvement if corrupt practices were curbed.

United States US: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data was reported at 0.781 % in 2013. This records a decrease from the previous number of 0.856 % for 2012. United States US: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data is updated yearly, averaging 0.880 % from Dec 1995 (Median) to 2013, with 18 observations. The data reached an all-time high of 1.078 % in 2000 and a record low of 0.724 % in 2008. United States US: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Poverty. Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure, expressed as a percentage of a total population of a country; ; Wagstaff et al. Progress on catastrophic health spending: results for 133 countries. A retrospective observational study, Lancet Global Health 2017.; Weighted Average;

Background: For decades, health targeted aid in the form of development assistance for health has been an important source of financing health sectors in developing countries. Health sectors in Sub Saharan countries in general and Ethiopia in particular, are even more heavily reliant upon donors. Consequently, a more audible donors support to health sectors was seen during the last four decades, consistent with the donor's response to the global goal of Alma-Ata declaration of "health for all by the year 2000" through primary health care in 1978. Ever since, a massive surge of development assistance for health has followed the out gone of the 2015 United Nations Millennium Declaration Goals in which three out of the eight goals were directly related to health. In spite of the long history of health targeted aid, with an ever increasing volumes, there is an increasing controversy on the extent to which health targeted aid is producing the intended health outcomes in the recipient countries. Despite the vast empirical literatures considering the effect of foreign development aid on economic growth of the recipient countries, systematic evidence that health sector targeted aid improves health outcomes is relatively scarce. The main contribution of this study is, therefore, to present a comprehensive country level, and cross-country evidences on the effect of development assistance for health on health outcomes. Objectives: The overall objective of this study was to analyze the effect of development assistance for health on health outcomes in Ethiopia, and in Sub Saharan Africa. Methods: For the Ethiopian (country level) study, a dynamic time series data analytic approach was employed. A retrospective sample of 36-year observations from 1978 to 2013 was analyzed using an econometric technique - vector error correction model. Beside including time dependency between the variables of interest and allowing for stochastic trends, the model provides valuable information on the existence of long-run and short-run relationships among the variables under study. Furthermore, to estimate the co-integrating relations and the other parameters in the model, the standard procedure of Johansen's approach was used. While development assistance for health expenditure was used as an explanatory variable of interest, life expectancy at birth was used as a dependent variable for the fact that it has long been used with or without mortality measures as health status indicators in the literatures.In the Sub Saharan Africa (cross-country level) study, a dynamic panel data analytic approach was employed using fixed effect, random effect, and the first difference-generalized method of moments estimators in the period confined to the year 1995-2013 over the cross section of 43 SSA countries. While development assistance for health expenditure was used as an explanatory variable of interest here again, infant mortality rate was used for health status measure done for its advantage over other mortality measures in cross-country studies. Results: In Ethiopia, the immediate one and two prior year of development assistance for health was shown to have a significant positive effect on life expectancy at birth. Other things being equal, an increase of development assistance for health expenditure per capita by 1% leads to an improvement in life expectancy at birth by about 0.026 years (P=0.000) in the immediate year following the period, and 0.008 years following the immediate prior two years period (P= 0.025). Similarly, in Sub-Saharan Africa, development assistance for health was found to have a strong negative effect on the reduction of infant mortality rate. The estimates of the study result indicated that during the covered period of study, in the region, a 1% increase in development assistance for health expenditure, which is far less than 10 cents per capita at the mean level, saves the life of two infants per 1000 live births (P=0.000). Conclusion: Contrary to the views of health aid skeptics, this study indicates strong favorable effect of development assistance for health sector in improving health status of people in Sub Saharan Africa in general and the Ethiopia in particular. Recommendations: The policy implication of the current findings is that development assistance for health sector should continue as an interim necessity means. However, domestic health financing system should also be sought, as the targeted countries cannot rely upon external resources continuously for improving the health status of the population. At the same time, the current development assistance stakeholders assumption of targeting facility based primary health care provision should be augmented by a more strong parallel strategy of improving socioeconomic status of the population that promotes sustainable improvement of health status in the targeted countries.

Title: The impact of COVID-19 on chronic care patients health care utilization and health outcomes in Haiti, Malawi, Mexico and Rwanda Original data source: Electronic Medical Records Date range: March 1st, 2019-Feb 28th, 2021 Geographic region: Non-representative subnational regions of Haiti, Malawi, Mexico, and Rwanda Clinical populations: Diabetes, HIV, and hypertension patients Level of data: Aggregated by country, sex, age category, clinical population, and pre- vs post-COVID-19 period Size of the data: 35 KB Research question/s that use the dataset 1. Has the COVID-19 pandemic changed the risk of poor clinical outcomes among chronic care patients living with HIV, cardiovascular disease and diabetes programs in Haiti, Malawi, Mexico and Rwanda? 2. Among these patients, how has care utilization changed during the COVID-19 pandemic? Useful Links https://icoda-research.org/project/dp-pih-covco/

Data access information: In order to request access to data, please contact Jean Claude Mugunga, jmugunga@pih.org, with a description of your study team, your research questions, and which countr(ies) and clinical program(s) you would like data for. Note that Dr. Mugugna will reach out to representatives from each country you request data from for approval before sharing the data.

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

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

Health Care Information System Market Outlook

The global Health Care Information System market size was valued at approximately USD 90 billion in 2023 and is projected to reach USD 190 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.5% during the forecast period. Factors such as the increasing adoption of digital health solutions, rising demand for accurate and timely patient information, and government initiatives promoting the deployment of electronic health records (EHR) are driving this growth.

One of the primary growth drivers for the Health Care Information System market is the increasing adoption of electronic medical records (EMRs) and electronic health records (EHRs). These systems have revolutionized the way patient data is stored, accessed, and analyzed, leading to improved patient outcomes and streamlined healthcare operations. The integration of advanced technologies like AI and machine learning with these systems further enhances their capabilities, enabling predictive analytics and better decision-making in clinical settings.

Another significant factor contributing to market growth is the rising need for efficient healthcare management systems. With an increasing global population and the prevalence of chronic diseases, healthcare providers are under immense pressure to deliver high-quality care while optimizing resources. Health Care Information Systems offer solutions for efficient patient management, billing, scheduling, and resource allocation, thereby enhancing the overall efficiency of healthcare delivery models.

Additionally, governmental policies and incentives aimed at digitizing healthcare infrastructures are playing a crucial role in market expansion. Various governments around the world are implementing regulations and providing financial incentives to encourage the adoption of health care information systems. This regulatory push is particularly strong in regions such as North America and Europe, where governments are focused on improving healthcare quality and patient safety through the use of digital solutions.

From a regional perspective, the Asia Pacific region is expected to witness substantial growth over the forecast period. This growth can be attributed to the rising investments in healthcare infrastructure, increasing awareness about digital health solutions, and the growing focus on improving healthcare services in countries like China and India. Moreover, the region's large population base and the increasing prevalence of lifestyle-related diseases provide a significant market opportunity for health care information systems.

The integration of a Healthcare Decision Support System (HDSS) within health care information systems is becoming increasingly vital. These systems provide clinicians with critical insights derived from patient data, enabling more informed decision-making processes. By leveraging data analytics and evidence-based guidelines, HDSS can assist healthcare providers in diagnosing conditions, selecting appropriate treatments, and managing patient care more effectively. The adoption of HDSS is driven by the need to improve patient outcomes and reduce the incidence of medical errors, which are often attributed to information gaps and cognitive overload among healthcare professionals. As healthcare systems become more complex, the role of decision support systems in ensuring quality care and operational efficiency cannot be overstated.

Component Analysis

The Health Care Information System market can be segmented by components into software, hardware, and services. The software segment is expected to dominate the market due to the increasing adoption of various applications such as EHRs, clinical decision support systems, and practice management software. These software solutions are essential for managing patient data, enhancing clinical workflows, and ensuring compliance with regulatory standards. The rapid advancements in software technologies, including AI and machine learning, are further driving the adoption of health care information systems.

In contrast, the hardware segment, which includes computing devices, storage devices, and networking equipment, plays a crucial role in the deployment and functioning of healthcare information systems. While hardware is essential for the infrastructure, its market share is relatively smaller compared to software due to the higher frequency of software upgrades and updates. However, the d

Data set from the article Van Bulck L, Luyckx K, Goossens E, Apers S, Kovacs AH, Thomet C, Budts W, Sluman MA, Eriksen K, Dellborg M, Berghammer M, Johansson B, Caruana M, Soufi A, Callus E, Moons P. Patient-reported outcomes of adults with congenital heart disease from eight European countries: scrutinising the association with healthcare system performance. Eur J Cardiovasc Nurs. 2019 Aug;18(6):465-473. doi: 10.1177/1474515119834484. Epub 2019 Feb 26. PMID: 30808198.

This is the abstract:

Background: Inter-country variation in patient-reported outcomes of adults with congenital heart disease has been observed. Country-specific characteristics may play a role. A previous study found an association between healthcare system performance and patient-reported outcomes. However, it remains unknown which specific components of the countries' healthcare system performance are of importance for patient-reported outcomes.

Aims: The aim of this study was to investigate the relationship between components of healthcare system performance and patient-reported outcomes in a large sample of adults with congenital heart disease.

Methods: A total of 1591 adults with congenital heart disease (median age 34 years; 51% men; 32% simple, 48% moderate and 20% complex defects) from eight European countries were included in this cross-sectional study. The following patient-reported outcomes were measured: perceived physical and mental health, psychological distress, health behaviours and quality of life. The Euro Health Consumer Index 2015 and the Euro Heart Index 2016 were used as measures of healthcare system performance. General linear mixed models were conducted, adjusting for patient-specific variables and unmeasured country differences.

Results: Health risk behaviours were associated with the Euro Health Consumer Index subdomains about patient rights and information, health outcomes and financing and access to pharmaceuticals. Perceived physical health was associated with the Euro Health Consumer Index subdomain about prevention of chronic diseases. Subscales of the Euro Heart Index were not associated with patient-reported outcomes.

Conclusion: Several features of healthcare system performance are associated with perceived physical health and health risk behaviour in adults with congenital heart disease. Before recommendations for policy-makers and clinicians can be conducted, future research ought to investigate the impact of the healthcare system performance on outcomes further.

Objectives: To identify and map all trials in maternal health conducted in low- and middle-income countries (LMIC) over the 10-year period 2010-2019, to identify geographical and thematic trends, as well as compare to global causes of maternal death and pre-identified priority areas. Design: Systematic scoping review. Primary and secondary outcome measures: Extracted data included location, study characteristics and whether trials corresponded to causes of mortality and identified research priority topics. Results: Our search identified 7,269 articles, 874 of which were included for analysis. Between 2010 and 2019, maternal health trials conducted in LMICs more than doubled (50 to 114). Trials were conducted in 61 countries – 231 trials (26.4%) were conducted in Iran. Only 225 trials (25.7%) were aligned with a cause of maternal mortality. Within these trials, pre-existing medical conditions, embolism, obstructed labour, and sepsis were all under-represented when compared with number of maternal deaths globally. Large numbers of studies were conducted on priority topics such as labour and delivery, obstetric haemorrhage, and antenatal care. Hypertensive disorders of pregnancy, diabetes, and health systems and policy – despite being high-priority topics – had relatively few trials. Conclusion: Despite trials conducted in LMICs increasing from 2010 to 2019, there were significant gaps in geographical distribution, alignment with causes of maternal mortality, and known research priority topics. The research gaps identified provide guidance and insight for future research conducted in low-resource settings. Methods With support from an information specialist, a search strategy was devised to capture eligible studies (Supplemental Table 1). Search terms for maternal and perinatal health were derived from search strategies used by Cochrane Pregnancy and Childbirth to maintain and update their specialised register. We consulted the search filters developed by Cochrane EPOC to identify search terms relating to LMICs. The search strategy was applied to the Cochrane Central Register of Controlled Trials (CENTRAL), which retrieves records from PubMed/MEDLINE, Embase, CINAHL, ClinicalTrials.gov, WHO’s International Clinical Trials Registry Platform (ICTRP), KoreaMed, Cochrane Review Group’s Specialised Registers, and hand-searched biomedical sources. Searching CENTRAL directly had the benefit of restricting search results to trials only, keeping the volume of citations to screen to a manageable level. Trial register records from ClinicalTrials.gov and WHO ICTRP were not included in the records retrieved from CENTRAL. The search was conducted on 1 May 2020. Citation management, identification of duplicates, and screening articles for eligibility were conducted using EndNote and Covidence. Two reviewers independently screened titles and abstracts of all retrieved citations to identify those that were potentially eligible. Full texts for these articles were accessed and assessed by two independent reviewers according to the eligibility criteria. At both steps, any disagreements were resolved through discussion or consulting a third author. Data collection and analysis For each included trial we extracted information on title, author, year of publication, location where the trial was conducted (country and SDG region), unit of randomisation (individual or cluster), category of intervention, intervention level (public health, community, primary care, hospital, and health system), and category of the primary outcome(s). The intervention and outcome categories were adapted from Cochrane’s list of ‘higher-level categories for interventions and outcomes’. For trials with more than one primary outcome, we identified a single, most appropriate outcome category through discussion and consensus amongst review authors. The level of intervention was determined based on the level of the healthcare system that the trial was primarily targeting – for example, trials recruiting women at an antenatal clinic were classified as primary care level. Public health and preventative care were defined as interventions for those in the community who were well, while home; and community care was defined as interventions for those in the community who were unwell. Based on the trial’s primary objective, we tagged each trial to one of 35 maternal health topics, as well as classified them by relevance to a cause of maternal death identified by Say et al in their global systematic analysis (Box 1). Included trials were additionally categorised into global research priority topics identified by Souza et al and Chapman et al. The research priorities identified by Souza et al were ranked based on the distribution of maternal health themes across the 190 priority research questions – i.e., the theme with the most research questions was considered the highest-ranked priority topic. This mirrored the process used by Chapman et al, where research topics with the greatest representation within the 100 research questions, based on percentage, were given the highest rank. For each trial identified in our review, we used the variables extracted to classify it according to priority topics identified in Souza et al or Chapman et al, where possible (Box 1). All data were extracted by two independent reviewers, with results compared to ensure consistency and any disputes resolved through discussion or consultation with a third author. As this was a scoping review, we did not perform quality assessments on individual trials. We conducted descriptive analyses using Excel to determine frequencies of extracted variables and used line graphs to explore trends. We assessed trends over time using proportions of each variable within studies available for a given year. While we initially planned to look at trends in individual countries and interventions, many had few or no data points.

Objectives Value-based Health Care (VBHC) is a health system reform gradually being implemented in health systems worldwide. A previous national-level survey has shown that Latin American countries were in the early stages of alignment with VBHC. Data at the healthcare providers level are lacking. This study aim was to investigate how healthcare providers in five Latin American countries are implementing the Value Agenda. Design Mixed-methods research was conducted using online questionnaire, semi-structured interviews (from December of 2018 to June of 2020), and analyses of aggregated data and documents. Statistical analysis was performed using Fisher's exact test. Univariate analysis was used to compare organizations in relation to the implementation of VBHC initiatives. P value ≤0.05 was considered significant. Participants Top and middle-level executives from 70 healthcare provider organizations from Argentina, Brazil, Chile, Colombia and Mexico. Results From a total of 172 initiatives referred by 55 participants, 58 referred by 33 participants were aligned with the value agenda and focused on care delivery organization (56.9%), outcomes measurement (22.4%), cost measurement (10.3%) and bundled payments (10.3%). Although fee-for-service predominated, one third of providers were experimenting with alternative payment models. Univariate analysis showed that specialty hospitals (p=0.05), a high level of alignment with care delivery organization (p<0,01) and outcomes measurement (p=0.01), implementation of ICHOM standard sets (p<0.01), and participation in alternative payment models were associated with VBHC implementation (p=0.01). Conclusions A wide variation in the level of implementation of the value agenda existed across participating providers. A list of initiatives was produced that may provide insights for different stakeholders. Scalability of such initiatives will demand investments on education of stakeholders and on systematic measurement and use of outcomes and cost data. Further research is needed to identify successful implementation cases that may serve as regional benchmark for other Latin American providers advancing with VBHC. Mixed-methods research combining both qualitative and quantitative techniques were used. Quantitative methods included an online questionnaire to assess the level of implementation of the value agenda components and to map VBHC initiatives, and analyses of aggregated data on the initiatives referred in the interview. Qualitative methods included semi-structured interviews and analysis of relevant documents, including meeting notes and published documents. Online surveys and interviews were applied between December of 2018 and June of 2020. From a total of 182 organizations considered to participate in the study, 71 signed the written consent. Two organizations requested to participate as a single organization, as they work as a single management and care provider, which resulted in a final sample of 70 participants. Quantitative and qualitative data were analyzed using descriptive statistics. Fisher's exact test was performed to compare organizations that had implemented VBHC initiatives with those that had not implemented. Univariate analysis was used to identify differences between the two groups in relation to VBHC implementation. To compare organizations regarding their level of alignment with the value agenda, answers to the online survey were transformed into binary variables, where ‘yes’ (high level of alignment) was considered if options ‘a or b’ had been selected, and ‘no’ (low level of alignment) for all other options. To keep data anonymized all information regarding country, size, and organization profiles were also turned into binary variables. A dictionary of terms to describe variables is available as part of the spreadsheet.

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

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

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

Artificial Intelligence (AI) in Medical Market Outlook

The global market size for Artificial Intelligence (AI) in the medical sector was valued at approximately $5.2 billion in 2023 and is expected to reach around $45.2 billion by 2032, growing at a CAGR of 26.8% during the forecast period. This remarkable growth can be attributed to several factors, including advancements in AI technology, increasing healthcare data, and the rising demand for personalized medicine.

One of the primary growth factors for AI in the medical market is the rapid advancement in AI technologies such as machine learning, natural language processing, and computer vision. These technologies enable healthcare providers to analyze vast amounts of data more effectively, leading to improved diagnostic accuracy and better patient outcomes. The integration of AI in medical imaging, for example, aids radiologists in detecting anomalies much earlier, thus facilitating timely intervention and treatment.

Another significant driver is the growing volume of healthcare data generated from electronic health records (EHRs), wearable devices, and genomics. AI systems are highly efficient at processing and analyzing this data to extract meaningful insights, which can be used for predictive analytics and early disease detection. This capability not only enhances patient care but also contributes to the operational efficiency of healthcare providers by streamlining administrative tasks and reducing the risk of human error.

The rising demand for personalized medicine is also a crucial factor driving the market. AI algorithms can analyze individual patient data to provide customized treatment plans, improving the efficacy of medical interventions. This approach is particularly beneficial in oncology, where personalized treatment plans based on genetic profiling can significantly improve patient outcomes. Furthermore, AI's role in drug discovery and development is accelerating the process of bringing new drugs to market, thus addressing unmet medical needs more rapidly.

Regionally, North America holds the largest share of the AI in medical market, primarily due to the high adoption rate of advanced technologies and substantial investments in healthcare infrastructure. The presence of key market players and extensive research activities further bolster the market in this region. Europe follows closely, with significant contributions from countries like Germany, the UK, and France, which have well-established healthcare systems and a strong focus on innovation. The Asia Pacific region is anticipated to witness the highest growth rate, driven by increasing healthcare expenditure, growing awareness of AI applications in healthcare, and supportive government policies.

Component Analysis

The AI in medical market can be segmented by component into software, hardware, and services. The software segment holds the largest market share and is expected to continue its dominance during the forecast period. This segment includes AI algorithms, platforms, and analytical tools that are crucial for data analysis and decision-making processes in medical applications. The growing adoption of AI-based software solutions for diagnostic and predictive analytics is a key driver for this segment.

The hardware segment, although smaller than software, is also experiencing significant growth. This segment comprises AI-enabled medical devices, sensors, and computing infrastructure necessary to support AI applications. The increasing demand for advanced imaging systems, robotic surgical instruments, and AI-integrated diagnostic tools is propelling the growth of this segment. Furthermore, advancements in hardware technologies, such as the development of high-performance GPUs and specialized AI chips, are enhancing the capabilities of AI systems in healthcare.

The services segment encompasses various support services required for the implementation and maintenance of AI systems in medical settings. This includes consulting, integration, and training services provided by vendors to ensure the smooth deployment and operation of AI technologies. The growing need for specialized skills and expertise to manage AI systems, along with the rising trend of outsourcing these services, is driving the expansion of this segment. Additionally, ongoing support and maintenance services are essential to keep AI systems updated and functioning optimally.

Within the services segment, managed services are gaining traction as healthcare providers seek to minimize the complexi