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.

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

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

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

ObjectiveAccess to healthcare is mostly contingent on out-of-pocket spending (OOPS) by health seekers, particularly in low- and middle-income countries (LMICs). This would require many LMICs to raise enough funds to achieve universal health insurance coverage. But, are individuals or households willing to pay for health insurance, and how much? What factors positively affect WTP for health insurance? We wanted to examine the evidence for this, through a review of the literature.MethodsWe systematically searched databases up to February 2016 and included studies of individual or household WTP for health insurance. Two authors appraised the identified studies. We estimated the WTP as a percentage of GDP per capita, and adjusted net national income per capita of each country. We used meta-analysis to calculate WTP means and confidence intervals, and vote-counting to identify the variables that more often affected WTP.Result16 studies (21 articles) from ten countries met the inclusion criteria. The mean WTP of individuals was 1.18% of GDP per capita and 1.39% of adjusted net national income per capita. The corresponding figures for households were 1.82% and 2.16%, respectively. Increases in family size, education level and income were consistently correlated with higher WTP for insurance, and increases in age were correlated with reduced WTP.ConclusionsThe WTP for healthcare insurance among rural households in LMICs was just below 2% of the GPD per capita. The findings demonstrate that in moving towards universal health coverage in LMICs, governments should not rely on households' premiums as a major financing source and should increase their fiscal capacity for an equitable health care system using other sources.

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

ObjectivesNational health insurance is now common in most developed countries. This study reviews the evidence and synthesizes the cost-effectiveness information for national health insurance or disability insurance programs across high-income countries.Data sourcesA literature search using health, economics and systematic review electronic databases (PubMed, Embase, Medline, Econlit, RepEc, Cochrane library and Campbell library), was conducted from April to October 2015.Study selectionTwo reviewers independently selected relevant studies by applying screening criteria to the title and keywords fields, followed by a detailed examination of abstracts.Data extractionStudies were selected for data extraction using a quality assessment form consisting of five questions. Only studies with positive answers to all five screening questions were selected for data extraction. Data were entered into a data extraction form by one reviewer and verified by another.Evidence synthesisData on costs and quality of life in control and treatment groups were used to draw distributions for synthesis. We chose the log-normal distribution for both cost and quality-of-life data to reflect non-negative value and high skew. The results were synthesized using a Monte Carlo simulation, with 10,000 repetitions, to estimate the overall cost-effectiveness of national health insurance programs.ResultsFour studies from the United States that examined the cost-effectiveness of national health insurance were included in the review. One study examined the effects of medical expenditure, and the remaining studies examined the cost-effectiveness of health insurance reforms. The incremental cost-effectiveness ratio (ICER) ranged from US$23,000 to US$64,000 per QALY. The combined results showed that national health insurance is associated with an average incremental cost-effectiveness ratio of US$51,300 per quality-adjusted life year (QALY). Based on the standard threshold for cost-effectiveness, national insurance programs are cost-effective interventions.ConclusionsAlthough national health insurance programs have been introduced in most developed countries, only a few studies have examined their cost-effectiveness. All the selected studies revealed strong evidence to support health insurance programs or health reforms in the United States. The average ICER in this study is below the standard threshold for cost-effectiveness used in the US. The small number of relevant studies is the main limitation of this study.

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

Additional file 2. Questionnaire. Questionnaire sent to country agents to fill in.

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

The spreadsheet provides the list of indicators reported by the national experts to assess the quality of child care in the relevant countries along with those gathered from official documents provided by the experts. It has been adopted to the Paper 'Variability in the assessment of childcare in 30 European countries'.

BackgroundTo promote shared digital health best practices in a global context, as agreed within the Global Digital Health Partnership (GDHP), one of the most important topics to evaluate is the ability to detect what participating countries believe to be priorities suitable to improve their healthcare systems. No previously published scientific papers investigated these aspects as a cross-country comparison.ObjectiveThe aim of this paper is to present results concerning the priorities identification section of the Evidence and Evaluation survey addressed to GDHP members in 2021, comparing countries’ initiatives and perspectives for the future of digital health based on internationally agreed developments.MethodsThis survey followed a cross-sectional study approach. An online survey was addressed to the stakeholders of 29 major countries.ResultsTen out of 29 countries answered the survey. The mean global score of 3.54 out of 5, calculated on the whole data set, demonstrates how the global attention to a digital evolution in health is shared by most of the evaluated countries.ConclusionThe resulting insights on the differences between digital health priority identification among different GDHP countries serves as a starting point to coordinate further progress on digital health worldwide and foster evidence-based collaboration.

The average for 2021 based on 10 countries was 46.11 index points. The highest value was in Singapore: 130.04 index points and the lowest value was in Laos: 21.7 index points. The indicator is available from 2017 to 2021. Below is a chart for all countries where data are available.

International Health Insurance Market Outlook

The global market size of the International Health Insurance market reached approximately USD 25 billion in 2023 and is projected to soar to a staggering USD 50 billion by 2032, exhibiting a robust CAGR of 7.9% during the forecast period. The significant growth factor contributing to this market is the increasing awareness and need for comprehensive healthcare coverage among individuals and corporates alike. The surge in medical costs, global travel, expatriation, and the rising prevalence of chronic diseases are some of the pivotal drivers fueling this market's expansion.

One of the primary growth factors is the globalization of the workforce. With the growing trend of multinational corporations, many employees are frequently stationed abroad. This has led to a higher demand for international health insurance plans, as they offer a safety net for employees against health-related uncertainties in foreign lands. Furthermore, the increase in international students pursuing education abroad also significantly contributes to this demand. Educational institutions and parents alike are keen on ensuring that students have adequate health coverage during their stay in foreign countries.

Another critical growth driver is the rising healthcare costs worldwide. Medical inflation is a significant concern, making it imperative for individuals and families to opt for health insurance plans that offer international coverage. With the healthcare systems in developed nations often being more expensive, international health insurance provides a crucial financial buffer. This ensures that policyholders can access high-quality medical care without facing financial hardships. Additionally, the increasing prevalence of lifestyle-related diseases such as diabetes, hypertension, and cardiovascular conditions necessitates continuous medical attention, further boosting the market.

The technological advancements in the insurance sector cannot be overlooked as a significant growth factor. Digital platforms and online distribution channels have made it easier for consumers to compare and purchase international health insurance plans. The convenience of online services, coupled with the availability of customized plans, has played a substantial role in attracting a broader customer base. Insurers are also leveraging data analytics and AI to offer personalized services and improve customer experiences, thereby enhancing the market's appeal.

Hospital Cash Benefit Insurances have emerged as a valuable addition to the international health insurance landscape. These plans provide policyholders with a fixed daily cash benefit during hospitalization, which can be used to cover out-of-pocket expenses that are not typically covered by standard health insurance. This includes costs such as transportation, accommodation for family members, and other incidental expenses that arise during a hospital stay. The flexibility offered by Hospital Cash Benefit Insurances makes them an attractive option for individuals seeking additional financial security during medical emergencies. As healthcare costs continue to rise globally, these insurances offer a practical solution to manage unforeseen expenses, thereby enhancing the overall appeal of comprehensive health insurance packages.

Regionally, North America and Europe dominate the international health insurance market due to the high number of expatriates, students, and travelers. The well-established healthcare infrastructure and stringent regulatory frameworks in these regions ensure high standards of service, thus making them attractive markets. However, the Asia Pacific region is expected to witness the highest growth rate over the forecast period. The increasing middle-class population, rising healthcare awareness, and economic growth in countries like China and India are key factors driving the market in this region.

Plan Type Analysis

The international health insurance market's segmentation by plan type includes individual plans, family plans, group plans, senior citizen plans, and others. Individual plans offer tailor-made coverage for single policyholders, addressing their specific healthcare needs. This segment is particularly popular among expatriates and international students, providing comprehensive coverage without tying policies to families or groups. The flexibility and customization options available in individual plans make them highly attractive,

Abstract

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

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

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

Geographic coverage

National

Analysis unit

Health facilities and healthcare providers

Universe

All health facilities providing primary-level care

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

Mode of data collection

Face-to-face [f2f]

Research instrument

The SDI Health Survey Questionnaire consists of four modules:

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

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

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

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

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

Cleaning operations

Quality control was performed in Stata.

Use of absolute and derived values in assessing Population health and the activities of healthcare

Submitted by Riya Patil & Rutuja Sonar, to Moldoev Murzali Ilyazovich Osh state University

ABSTRACT

In contrast, derived values involve the use of statistical techniques to calculate indirect indicators from absolute values. These include metrics like disability-adjusted life years (DALYs), quality-adjusted life years (QALYs), and health-adjusted life expectancy (HALE). Derived values are instrumental in understanding the broader context of population health, as they often combine both mortality and morbidity data to reflect the overall burden of disease.

In healthcare institutions, these values are integral in guiding resource allocation, evaluating the effectiveness of interventions, and shaping policies aimed at improving health outcomes. While absolute values provide essential raw data, derived values offer nuanced insights into the quality and long-term impact of healthcare services. Together, they form a comprehensive approach to measuring and improving population health, helping healthcare institutions prioritize actions and allocate resources more effectively.

This paper explores the role of absolute and derived values in assessing population health and their relevance to healthcare institutions, examining how both types of values support decision-making and influence health policy.

Keywords: Population health, absolute values, derived values, healthcare institutions, mortality rates, morbidity, Disability-Adjusted Life Years (DALYs), Quality-Adjusted Life Years (QALYs), Health-Adjusted Life Expectancy (HALE), health policy, healthcare interventions.

INTRODUCTION

Use of Absolute and Derived Values in Assessing Population Health and the Activities of Healthcare Institutions**

Population health is a key focus of public health systems and healthcare institutions worldwide. Assessing the health of a population requires robust metrics to understand the current state of health, identify risks, and track trends over time. One of the essential tools in evaluating population health is the use of **absolute values** and **derived values**. These metrics offer complementary insights into both the health status of individuals within a population and the effectiveness of healthcare interventions.

**Absolute values** are straightforward measures that provide direct data points, such as the total number of people suffering from a specific disease, the number of hospital admissions, or the total expenditure on healthcare services. These values are critical for understanding the scale of health issues and resource needs within a community.

**Derived values**, on the other hand, are ratios or indices calculated from absolute values. They allow for more meaningful comparisons across populations, time periods, or geographical areas. Examples include rates such as morbidity or mortality rates, life expectancy, and disease prevalence, which are essential for assessing public health outcomes and guiding healthcare policy and decision-making.

By integrating both absolute and derived values, healthcare institutions can gain a comprehensive picture of population health, identify areas for improvement, allocate resources more efficiently, and track the effectiveness of healthcare initiatives. This approach helps ensure that healthcare systems are responsive to the needs of the population and can adapt to emerging health challenges.

METHODOLOGY

Method and analysis which is performed by the google worksheet and google forms

Absolute Values in Assessing Population Health:

Absolute values refer to raw, unadjusted data points that provide a direct measure of a population's health status. These values are fundamental for initial assessments, as they provide baseline data for various health indicators.

Definition and Examples

Absolute values refer to concrete figures that represent the total counts or occurrences of specific health events or conditions. For example:

Total Mortality Rate: The number of deaths in a population over a specific time period (e.g., deaths per 100,000 people).

Prevalence Rates: The proportion of individuals in a population diagnosed with a specific condition at a particular time (e.g., diabetes prevalence).

Incidence Rates: The number of new or newly diagnosed cases of a disease over a given period (e.g., cancer incidence).

Life Expectancy: The average number of years a person is expected to live based on current mortality rates.

Use in Population Health

Health Monitoring: Absolute values allow public health authorities to monitor trends in population health, such as increases in mortality or the spread of disease.

Resource Allocation: These values help in determining the burden of disease in different populations, aiding in the efficient distribution of healthcare resources.

Derived Values in Assessing Population Health

Derived values involve the use of mathematical formulas or statistical techniques to adjust or combine absolute values to create composite indices or ratios that provide deeper insights into health outcomes and healthcare activities.

Definition

Derived values are statistical measures that offer context to absolute

by relating them to population characteristics. Common examples include:

Age-Standardized Mortality Rate: Adjusts the mortality rate for differences in the age structure of different populations, allowing comparisons between populations with different age distributions.

Disability-Adjusted Life Years (DALY): A composite measure that combines years of life lost due to premature death and years lived with disability. DALY provides a more comprehensive understanding of the burden of disease.

Quality-Adjusted Life Years (QALY): A measure used to evaluate the effectiveness of healthcare interventions by combining quantity and quality of life.

Health Inequality Index: Derived by comparing health disparities between different subgroups within a population.

Use in Population Health

Risk Assessment: Derived values like DALYs or QALYs enable healthcare providers and policymakers to assess the relative impact of different diseases or health conditions on the population’s overall health.

Health Outcomes Comparison: Derived values facilitate comparisons across different populations or regions, adjusting for factors like age, gender, or socioeconomic status.

Policy and Program Evaluation: Derived values are used to evaluate the effectiveness of public health interventions or healthcare programs, such as whether a vaccination program reduces disease burden over time.

Significance

Contextualizing Health Trends: Absolute values alone may not offer a clear picture. For instance, while an increase in the number of cancer cases might be alarming, derived values like the cancer incidence rate allow us to understand if the increase is due to an actual rise in cases or simply a result of population growth.

Comparative Analysis: Derived values are essential when comparing different populations or regions. For example, comparing the infant mortality rate in different countries provides insights into healthcare system performance, whereas absolute numbers may mislead without considering population size differences.

Evaluating Healthcare Efficiency: Derived values such as cost-effectiveness or patient outcomes per healthcare dollar provide insights into the efficiency of healthcare institutions. This helps identify areas of improvement in resource allocation and delivery of services.

Policy and Planning: Derived values play a crucial role in informing public health policies and healthcare strategies. For example, the quality-adjusted life year (QALY), derived from health outcome measures, is commonly used in health economics to assess the effectiveness of medical treatments and interventions.

Conclusion

Both absolute and derived values are integral to assessing population health and healthcare institution activities. Absolute values provide raw data, while derived values allow for deeper analysis, trends, and comparisons, giving a more comprehensive picture of health outcomes and healthcare performance.

REFERENCE

1.Kindig D, Stoddart G (March 2003). "What is population health?". American Journal of Public Health. 93 (3): 380–3. doi:10.2105/ajph.93.3.380. PMC 1447747. PMID 12604476.

2. McGinnis JM, Williams-Russo P, Knickman JR (2002). "The case for more active policy attention to health promotion". Health Aff (Millwood). 21 (2): 78–93. doi:10.1377/hlthaff.21.2.78. PMID 11900188.. See also National Academies Press free publication: The Future of Public Health in the 21st Century.

3. World Health Organization. 2006. Constitution of the World Health Organization – Basic Documents, Forty-fifth edition, Supplement, October 2006.

4. Jeffery RW. 2001. Public health strategies for obesity treatment and prevention. American Journal of Health Behavior 25:252–259.

5. Buunk BP, Verhoeven K. 1991. Companionship and support at work: a microanalysis of the stress-reducing features of social interactions. Basic and Applied Social Psychology 12:243–258.

6. CDC. 2001. a. CDC FactBook 2000/2001: Profile of the Nation's Health. Atlanta, GA: CDC.

7. What is the WHO definition of health? from the Preamble to the Constitution of WHO as adopted by the

BackgroundAcute gastroenteritis is a highly contagious disease demanding effective public health and clinical care systems for prevention and early intervention to avoid outbreaks and symptom deterioration. The Netherlands and Australia are both top-performing, high-income countries where general practitioners (GPs) act as healthcare gatekeepers. However, there is a lower annual incidence and per-case costs for childhood gastroenteritis in Australia. Understanding the systems and policies in different countries can lead to improvements in processes and care. Therefore, we aimed to compare public health systems and clinical care for children with acute gastroenteritis in both countries.MethodsA cross-country expert study was conducted for the Netherlands and Australia. Using the Health System Performance Assessment framework and discussions within the research group, two questionnaires (public health and clinical care) were developed. Questionnaires were delivered to local experts in the Netherlands and the state of Victoria, Australia. Data synthesis employed a narrative approach with constant comparison.ResultsIn Australia, rotavirus vaccination is implemented in a national program with immunisation requirements and legislation for prevention, which is not the case in the Netherlands. Access to care differs, as Dutch children must visit their regular GP before the hospital, while in Australia, children have multiple options and can go directly to hospital. Funding varies, with the Netherlands providing fully funded healthcare for children, whilst in Australia it depends on which GP (co-payment required or not) and hospital (public or private) they visit. Additionally, the guideline-recommended dosage of the antiemetic ondansetron is lower in the Netherlands.ConclusionsHealthcare approaches for managing childhood gastroenteritis differ between the Netherlands and Australia. The lower annual incidence and per-case costs for childhood gastroenteritis in Australia cannot solely be explained by the differences in healthcare system functions. Nevertheless, Australia’s robust public health system, characterized by legislation for vaccinations and quarantine, and the Netherland’s well-established clinical care system, featuring fully funded continuity of care and lower ondansetron dosages, offer opportunities for enhancing healthcare in both countries.

Abstract

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

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

The Nigeria SDI Health survey team visited a sample of 2,385 health facilities across Nigeria between July 2013 and January 2014. The survey team collected rosters covering 21,318 workers for absenteeism and assessed 5,017 health workers for competence using patient case simulations. The data is representative at the state level. The sample included 12 of 36 states in Nigeria due to logistical constraints.

Geographic coverage

Twelve states of Nigeria (Anambra, Bauchi, Bayelsa, Cross River, Ekiti, Imo, Kaduna, Kebbi, Kogi, Niger, Osun and Taraba)

Analysis unit

Health facilities and healthcare providers

Universe

All health facilities providing primary-level care in these twelve states.

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

Sampling deviation

The sample in Nigeria is not nationally representative, including 12 of 36 states.

Mode of data collection

Face-to-face [f2f]

Research instrument

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

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

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

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

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

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

Cleaning operations

Quality control was performed in Stata.

This survey is a follow-up to the 2009 survey with additions and specializations in key healthcare parameters/variables for the country as well as administrative units. The data were obtained from the Hellenic Statistical Authority. The purpose of this survey is to analyse the indicators relating to healthcare and compare them over time in order to draw conclusions on the development of healthcare in the country. The analysis concerns the comparative evolution over time of specific variables such as the number of beds, equipment and staff of the clinics, the number of doctors per specialty and the number of pharmacies.