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The study aims to determine whether there is a bidirectional causality relationship between health expenditures and per capita income in Brazil, Russia, India, China, South Africa and Turkey (BRICS+T).

For that purpose, the 2000–2018 period data of the variables were tested with the Kónya (2006) panel causality test. Additionally, the causality relationships between public and private health expenditures and per capita income were also investigated in the study.

According to the analysis results, there is no statistically significant causality relationship from total health expenditures and public health expenditures to per capita income in the relevant countries. Besides, there is a unidirectional causality relationship from private health expenditures to per capita income only in Turkey. On the other hand, a unidirectional causality relationship from per capita income to total health expenditures in China, Russia, Turkey and South Africa and from per capita income to public health expenditures in India, Russia, Turkey and South Africa were determined. Consequently, a causality relationship from per capita income to private health expenditures was found out in Russia and Turkey.

The variables are tested for the first time for BRICS+T countries, vis-à-vis the period under consideration and the method used.

This study aims to investigate the consequence of the quality of governance (QoG) in moderating the effect of healthcare spending on human development.

The authors employ a two-step Windmeijer finite sample-corrected system-generalized method of moments (sys-GMM) estimation technique on a panel dataset of 161 countries from 2005 to 2019. The authors use healthcare expenditure as the main explanatory variable and the Human Development Index (HDI) as the dependent variable and also consider voice and accountability (VnA), political stability and absence of terrorism (PSnAT), governance effectiveness (GoE), regulatory quality (ReQ), rules of law (RLaw) and control of corruption (CoC) dimensions of governance indicators as proxies of good governance. The authors develop a new measure of good governance from these six dimensions of governance using principal component analysis (PCA).

The authors empirically revealed that allocating more healthcare support alone is insufficient to improve human development. Individually, PSnAT has the highest net positive effect on health expenditure that helps to increase human welfare. Further, the corresponding interaction effect between expenditure and the Good Governance Index (GGI) is negative but insignificant for low-income countries (LICs); negative and statistically significant for sub-Saharan African (SSA) economies and positive but insignificant for South Asian nations.

This study is an in-depth analysis of how governance impacts the effectiveness of healthcare expenditure to ensure higher human development, particularly in a large panel of 161 countries. The authors have developed a new index of good governance and later extended the analysis by separating countries based on the income level and geographical location, which are utterly absent in existing literature.

This paper aims to evaluate the technical efficiency of the health-care systems in 21 selected middle-income countries during the period (2000–2017) and determine the source of inefficiency whether it is transient (short run) or persistent (long run).

The study uses the stochastic frontier analysis technique through employing the generalized true random effects model which overcomes the drawbacks of the previously introduced stochastic frontier models and allows for the separation between unobserved heterogeneity, persistent inefficiency and transient inefficiency.

Persistent efficiency is lower than the transient efficiency; hence, there are more efficiency gains that can be made by the selected countries by adopting long-term policies that aim at reforming the structure of the health-care system in the less efficient countries such as South Africa and Russia. The most efficient countries are Vietnam, Mexico and China which adopted a social health insurance that covers almost the whole population with the aim of increasing access to health-care services. Also, decentralization in health-care has assisted in adopting health-care policies that are suitable for both the rural and urban areas based on their specific conditions and health-care needs. A key success in the implementation of the adopted long-term policies by those countries is the continuous monitoring and evaluation of their outcomes and comparing them with the predefined targets and conducting any necessary modifications to ensure their movement in the right path to achieve their goals.

Although several studies have evaluated the technical efficiency both across and within countries using non-parametric (data envelopment analysis) and parametric (stochastic frontier analysis) approaches, to the best of the authors’ knowledge, this is the first attempt to evaluate the technical efficiency of selected middle-income countries during the period (2000–2017) using the generalized true random effects stochastic frontier analysis model.

Out-of-pocket (OOP) payments continue to be a major method of financing healthcare in many low- and middle-income countries including Malaysia. Although macro-level data show that this is a substantial percentage of national health expenditure, at the grassroots level, the amount spent on health by households remains unknown in Malaysia. The purpose of this paper is to assess the validity and reliability of an adapted-for-purpose questionnaire designed to capture urban household health expenditures (HHEs) among Malaysian households.

This two-part study assessed content validity of the questionnaire using three experts and the reliability of the questionnaire through a test-retest study among 50 OOP-paying patients followed up at one private primary care clinic in Kuala Lumpur. This study was approved by the Malaysian Research Ethics Committee (NMRR-16-172-29311-IIR).

The validity of the 83-item questionnaire was high, with an item content validity index of 1.00 and a scale content validity index average score of 1.0 agreed to among the evaluating experts. In the test-retest reliability study, the majority of the categorical questionnaire items had perfect agreement values (k=0.81-1.00). Continuous questionnaire items were also found to be highly reliable with no significant differences between the test-retest segments and high correlation coefficient values (intra-class correlation coefficient>0.7).

The HHE questionnaire had excellent content validity and very high test-retest reliability. The results of this study suggest that this questionnaire could be used in Malaysian studies to determine actual urban HHE which is a first step toward developing universal health coverage for all.

In order to assess the state of health of Europe’s children, or to appraise the systems and models of healthcare delivery, data about children are essential, with as much precision and accuracy as possible by small group characteristic. Unfortunately, the experience of the Models of Child Health Appraised (MOCHA) project and its scientists shows that this ideal is seldom met, and thus the accuracy of appraisal or planning work is compromised. In the project, we explored the data collected on children by a number of databases used in Europe and globally, to find that although the four quinquennial age bands are common, it is impossible to represent children aged 0–17 years as a legally defined group in statistical analysis. Adolescents, in particular, are the most invisible age group despite this being a time of life when they are rapidly changing and facing increasing challenges. In terms of measurement and monitoring, there is little progress from work of nearly two decades ago that recommended an information system, and no focus on the creation of a policy and ethical framework to allow collaborative analysis of the rich anonymised databases that hold real-world people-based data. In respect of data systems and surveillance, nearly all systems in European society pay lip service to the importance of children, but do not accommodate them in a practical and statistical sense.

The 30 MOCHA (Models of Child Health Appraised) countries are diverse socially, culturally and economically, and differences exist in their healthcare systems and in the scope and role of primary care. An economic analysis was undertaken that sought to explain differences in child health outcomes between countries. The conceptual framework was that of a production function for health, whereby health outputs (or outcomes) are assumed affected by several ‘inputs’. In the case of health, inputs include personal (genes, health behaviours) and socio-economic (income, living standards) factors and the structure, organisation and workforce of the healthcare system. Random effects regression modelling was used, based on countries as the unit of analysis, with data from 2004 to 2016 from international sources and published categorisations of healthcare system. The chapter describes the data deficiencies and measurement conundrums faced, and how these were addressed. In the absence of consistent indicators of child health outcomes across countries, five mortality measures were used: neonatal, infant, under five years, diabetes (0–19 years) and epilepsy (0–19 years). Factors found associated with reductions in mortality were as follows: gross domestic product per capita growth (neonatal, infant, under five years), higher density of paediatricians (neonatal, infant, under five years), less out-of-pocket expenditure (neonatal, diabetes 0–19), state-based service provision (epilepsy 0–19) and lower proportions of children in the population, a proxy for family size (all outcomes). Findings should be interpreted with caution due to the ecological nature of the analysis and the limitations presented by the data and measures employed.

This study aims at evaluating the technical efficiency (TE) of healthcare systems in the Arab region and exploring the key factors that affect the efficiency performance.

The study applies a two-stage Data Envelopment Analysis (DEA) approach to a sample of 20 Arab countries. In the first stage, a DEA model is used to calculate the TE scores of the examined healthcare systems in 2019 and 2010, following both the output and input orientations of efficiency. In the second stage, a censored Tobit model is estimated to investigate the determinants of healthcare efficiency.

DEA results of 2019 indicate that achievable efficiency gains of the Arab countries range from 0.4% to 16% under the output and input orientations, respectively. Six countries are efficient under both orientations. Although the average efficiency scores of the Arab countries have deteriorated between 2010 and 2019, Djibouti and Sudan had the greatest efficiency improvements between the two years. Bahrain, Mauritania, Morocco and Qatar proved to be efficient in 2010 and 2019 under the two orientations of efficiency and according to the two DEA specifications followed. The Tobit model reveals that corruption and government health expenditure tend to have an adverse impact on healthcare efficiency.

The author evaluates healthcare efficiency and healthcare's efficiency determinants in the Arab countries. Regardless Arab countries' diversity, these countries are facing common health challenges, including diminishing role of governments in healthcare financing; increased out-of-pocket healthcare spending; poor healthcare outputs and prevalence of health inequities resulting from weak governance institutions. Comparing the efficiency of healthcare systems between 2010 and 2019 gives insights on the potential impact of the Arab spring uprisings on healthcare efficiency. Moreover, examining the determinants of healthcare efficiency allows for better understanding of how to improve the efficiency of healthcare systems in the region.

The economic shock posed by the current COVID-19 outbreak brought out a worldwide public health emergency with a close relationship between the industrial marketing practices, the health level of society and its economic development. The purpose of this study is to analyse the industrial dynamics in health care and their impact on economic growth and health status.

To empirically investigate the relationship between growth and health, the authors use a data set drawn from 29 selected Organisation for Economic Co-operation and Development (OECD) countries over the period 2000 and 2019. Using panel regressions, the authors investigate the impact of the health-care industry measured in terms of health status, health expenditure, sales on pharmaceutical products, the number of persons working in health care and the coverage by private health insurances. Fixed effect and random effect regressions are used to estimate this model.

Overall, the results are suggestive of a nexus between the industrial marketing dynamics of health-care context and economic growth – both interacting and improving each other. As the quality of the health-care market enhances, the economy grows richer and the health status of the population improves considerably.

To support health-care markets in OECD countries, health policymakers need to formulate a long-term industrial health policy that addresses all the social and individual determinants of health.

To the best of the knowledge, this is the first study to provide a better understanding of the relationship between health-care industrial dynamics and economic growth in OECD countries along different dimensions.

Bipolar and depression disorders are some of the most common mental health disorders affecting millions of people in low-and middle-income countries, including those in Africa. These disorders are therefore major contributors to the burden of diseases and disability. While an enhancement in income is seen as a major approach towards reducing the burden of these disorders, empirical evidence to support this view in the African context is lacking. This study therefore aims to examine the effect of per capita income growth on bipolar and depression disorders across African countries.

The study uses data from secondary sources comprising 42 African countries over the period, 2002–2019, to achieve its objective. The prevalence of bipolar and major depressive disorders (depression) are used as the dependent variables, while per capita income growth is used as the main independent variable. The system Generalised Method of Moments regression is used as the estimation technique.

In the baseline, the authors find per capita income growth to be associated with a reduction in the prevalence of bipolar (coefficient: −0.001, p < 0.01) and depression (coefficient: −0.001, p < 0.1) in the short-term. Similarly, in the long-term, per capita income growth is found to have negative association with the prevalence of bipolar (coefficient: −0.059, p < 0.01) and depression (coefficient: −0.035, p < 0.1). The results are similar after robustness checks.

This study attempts at providing the first empirical evidence of the effect of per capita income growth on bipolar and depression disorders across several African countries.