Dataset Information

Nonlinear Granger Causality between Health Care Expenditure and Economic Growth in the OECD and Major Developing Countries.

ABSTRACT: Differing from previous studies ignoring the nonlinear features, this study employs both the linear and nonlinear Granger causality tests to examine the complex causal relationship between health care expenditure and economic growth among 15 Organisation for Economic Co-operation and Development (OECD) and 5 major developing countries. Some interesting findings can be obtained as follows: (1) For Australia, Austria, and UK, linear and nonlinear Granger causality does not exist between them. A unidirectional linear or nonlinear causality running from economic growth to health care expenditure can be found for Ireland, Korea, Portugal, and India. For these seven countries, health or fiscal policy related to health spending will not have an impact on economic growth; (2) For Belgium, Norway, and Mexico, only a unidirectional linear causality runs from health care expenditure to economic growth, while bidirectional linear causality can be found for Canada, Finland, Iceland, New Zealand, Spain, Brazil, and South Africa. Especially for the US, China, and Japan, a unidirectional nonlinear causality exists from health spending to economic growth. To improve the quality of national health, life quality and happiness, these 13 countries should actively look to optimise policy related to health care expenditure, such as by enhancing the efficiency of health costs to promote sustainable economic development.

SUBMITTER: Ye L

PROVIDER: S-EPMC6163394 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Publications

Nonlinear Granger Causality between Health Care Expenditure and Economic Growth in the OECD and Major Developing Countries.

Ye Liping L Zhang Xinping X

International journal of environmental research and public health 20180907 9

Differing from previous studies ignoring the nonlinear features, this study employs both the linear and nonlinear Granger causality tests to examine the complex causal relationship between health care expenditure and economic growth among 15 Organisation for Economic Co-operation and Development (OECD) and 5 major developing countries. Some interesting findings can be obtained as follows: (1) For Australia, Austria, and UK, linear and nonlinear Granger causality does not exist between them. A un ...[more]

PMID: 30205463

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Project description:BackgroundIt is a crucial task of brain science researches to explore functional connective maps of Biological Neural Networks (BNN). The maps help to deeply study the dominant relationship between the structures of the BNNs and their network functions.ResultsIn this study, the ideas of linear Granger causality modeling and causality identification are extended to those of nonlinear Granger causality modeling and network structure identification. We employed Radial Basis Functions to fit the nonlinear multivariate dynamical responses of BNNs with neuronal pulse firing. By introducing the contributions from presynaptic neurons and detecting whether the predictions for postsynaptic neurons' pulse firing signals are improved or not, we can reveal the information flows distribution of BNNs. Thus, the functional connections from presynaptic neurons can be identified from the obtained network information flows. To verify the effectiveness of the proposed method, the Nonlinear Granger Causality Identification Method (NGCIM) is applied to the network structure discovery processes of Spiking Neural Networks (SNN). SNN is a simulation model based on an Integrate-and-Fire mechanism. By network simulations, the multi-channel neuronal pulse sequence data of the SNNs can be used to reversely identify the synaptic connections and strengths of the SNNs.ConclusionsThe identification results show: for 2-6 nodes small-scale neural networks, 20 nodes medium-scale neural networks, and 100 nodes large-scale neural networks, the identification accuracy of NGCIM with the Gaussian kernel function was 100%, 99.64%, 98.64%, 98.37%, 98.31%, 84.87% and 80.56%, respectively. The identification accuracies were significantly higher than those of a traditional Linear Granger Causality Identification Method with the same network sizes. Thus, with an accumulation of the data obtained by the existing measurement methods, such as Electroencephalography, functional Magnetic Resonance Imaging, and Multi-Electrode Array, the NGCIM can be a promising network modeling method to infer the functional connective maps of BNNs.

Project description:BackgroundChronic migraine (CM) is a significant neurological condition affecting a substantial portion of the global population. The economic burden of CM includes both direct healthcare costs and indirect costs resulting from productivity losses and intangible impacts on patients' quality of life. However, there is limited research that comprehensively evaluates all cost components associated with CM, highlighting the need for a systematic review.MethodsWe conducted a systematic literature search in databases including MEDLINE, Embase, and CINAHL to identify studies estimating the cost of illness of chronic migraines. The search was restricted to English language articles published from inception to October 2021, and only findings from Organisation for Economic Co-operation and Development (OECD) countries were included. Methodology features and key findings were extracted from the studies, and reported costs were converted to GBP for cross-country comparisons.ResultsThirteen cost-of-illness studies on CM from various OECD countries were included in this review. The studies demonstrated substantial variations in monetary estimates, but consistently highlighted the considerable economic burden of CM. Direct costs, particularly hospitalisation and medication expenses, were identified as the highest contributors. However, indirect costs, such as productivity losses due to absenteeism and presenteeism, were often underexplored in the reviewed studies. Additionally, intangible costs related to emotional and social impacts on patients were largely overlooked.ConclusionChronic migraine imposes a significant economic burden on individuals, healthcare systems, and society. Policymakers and healthcare stakeholders should consider both direct and indirect cost components, as well as intangible costs, in developing targeted strategies for effective CM management and resource allocation. Further research focusing on comprehensive cost assessments and sensitivity analyses is needed to enhance the understanding of CM's economic implications and inform evidence-based healthcare policy decisions. Addressing these research gaps can alleviate the economic burden of CM and improve patient outcomes.

Dataset Information

Nonlinear Granger Causality between Health Care Expenditure and Economic Growth in the OECD and Major Developing Countries.

Publications

Nonlinear Granger Causality between Health Care Expenditure and Economic Growth in the OECD and Major Developing Countries.

Similar Datasets

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