Architecture of the health system as an enabler of better wellbeing.

INTRODUCTION: Health systems worldwide have heterogenous capacities and financing characteristics. No clear empirical evidence is available on the possible outcomes of these characteristics for population wellbeing. AIM: The study aims to provide empirical insight into health policy alternatives to support the development of health system architecture to improve population wellbeing. METHOD AND RESULTS: We developed an unsupervised neural network model to cluster countries and used the Human Development Index to derive a wellbeing model. The results show that no single health system architecture is associated with a higher level of population wellbeing. Strikingly, high levels of health expenditure and physical health capacity do not guarantee a high level of population wellbeing and different health systems correspond to a certain wellbeing level. CONCLUSIONS: Our analysis shows that alternative options exist for some health system characteristics. These can be considered by governments developing health policy priorities.

Toward an economy of wellbeing: The economic impact of the Welsh healthcare sector.

Population health and wellbeing is both a result, as well as a driver, of economic development and prosperity on global, European, national and sub-national (local) levels. Wales, one of the four United Kingdom (UK) nations, has shown a long-term commitment to sustainable development and achieving prosperity for all, providing a good example of both national and sub-national level, which can be useful for other European countries and regions. In this paper, the economic importance of the healthcare sector to the Welsh economy is explored. We use a large number of data sources for the UK and Welsh economy to derive an economic model for 2017. We estimate output, income, employment, value-added, and import multipliers of the healthcare sector. Results suggest that the healthcare sector has an above average contribution in four explored economic aspects of the Welsh economy (output, income, employment, value-added), according to its impact on the surrounding economic ecosystem. Also, it is below average regarding leaking through imports. The multipliers' values offer empirical evidence when deciding on alternative policy actions. Such actions can be used as a stimulus for encouraging regional development and post-COVID economic recovery. Our study refers to the Welsh healthcare sector's economic impact as a whole. Therefore, we suggest investigating the economic impact of individual healthcare providers in the future.

Reshaping the Healthcare Sector with Economic Policy Measures Based on COVID-19 Epidemic Severity: A Global Study.

Governments around the world are looking for ways to manage economic consequences of COVID-19 and promote economic development. The aim of this study is to identify the areas where the application of economic policy measures would enhance the resilience of societies on epidemic risks. We use data on the COVID-19 pandemic outcome in a large number of countries. With the estimation of multiple econometric models, we identify areas being a reasonable choice for economic policy intervention. It was found that viable remediation actions worth taking can be identified either for long-, mid-, or short-term horizons, impacting the equality, healthcare sector, and national economy characteristics. We suggest encouraging research and development based on innovative technologies linked to industries in healthcare, pharmaceutical, and biotech, promoting transformation of healthcare systems based on new technologies, providing access to quality healthcare, promoting public healthcare providers, and investing in the development of regional healthcare infrastructure, as a tool of equal regional development based on economic assessment. Further, a central element of this study, i.e. the innovative identification matrix, could be populated as a unique policy framework, either for latest pandemic or any similar outbreaks in future.

Two robust long short-term memory frameworks for trading stocks.

This paper aims to find a superior strategy for the daily trading on a portfolio of stocks for which traditional trading strategies perform poorly due to the low frequency of new information. The experimental work is divided into a set of traditional trading strategies and a set of long short-term memory networks. The networks incorporate general and specific trading patterns, where the former takes into account the universal decision factors for trading across many stocks, while the latter takes into account stock-specific decision factors. Our research shows that both long short-term memory networks, regardless of whether they are based on universal or stock-specific decision factors, significantly outperform traditional trading strategies. Interestingly, however, on average neither has the edge compared to the other, thus remaining ambivalent as to whether universality or specificality is to be preferred when it comes to designing long short-term memory networks for optimal trading.

The impact of the health-care sector on national economies in selected European countries.

Policy-makers face pressures to improve lives and safeguard public finances sustainably. In this analysis, we estimate the economic importance of the health-care sector in 19 European national economies. We use input-output tables for the year 2010 and sectoral data to estimate a set of multipliers: simple, total, truncated, type I and type II multipliers for output, income, value-added, employment and import multiplier. The analysis reveals similarities in the economic importance of the health-care sector for the national economies of the observed countries. Results suggest prevailing positive effects on national economies (value-added, employment and household income) when spending on health-care sector products and services increases, especially in comparison to the effects of increases in spending in other sectors. The importance of the health-care sector is connected to countries' levels of development; the benefits are especially promising in countries with lower levels of gross domestic product (GDP) per capita, where changes in the health-care sector have a larger impact on employment in the national economy than similar changes in more developed countries. The health-care sector therefore can play an important role as an instrument of economic policy.

Prediction of liver metastases after gastric cancer resection with the use of learning vector quantization neural networks.

OBJECTIVE: The aim of our study was to determine whether learning vector quantization neural networks could be used to predict liver metastases after a gastric cancer surgery. BACKGROUND: The prediction of tumor recurrence is invaluable for tailoring specific treatment and follow-up strategies for gastric cancer patients. At present, it is still impossible to make reliable predictions of tumor progression. The use of complex mathematical models such as neural networks has already been implemented for the study of various pathophysiological mechanisms, but to date they have never been used for predicting liver metastases after gastric cancer resection. METHODS: A total of 213 patients operated for gastric cancer between 1999 and 2005 were included in our study. They were stratified in a model development (140 patients) and validation group (73 patients). With the use of an auxiliary regression network, seven clinicopathological variables were selected to predict liver metastases. RESULTS: Forty-one patients developed liver metastases (19.2%). The longest follow-up was 2,754 days. Most liver metastases occurred in the first 799 days after discharge. All predictions were compared to actual recurrences with a two by two contingence table. The determined sensitivity and specificity for the development sample were 71 and 96.1%, respectively. The values for the test sample were 66.7 and 97.1%, respectively. The significance of the model was determined using various post-hoc tests, which all confirmed the effectiveness of our model. CONCLUSION: The presented model exhibited a high negative predictive value and reasonable high sensitivity for liver metastases. To improve sensitivity, the inclusion of more patients and perhaps biological markers is still necessary.

Irregularity test for very short electrocardiogram (ECG) signals as a method for predicting a successful defibrillation in patients with ventricular fibrillation.

A significant proportion of patients with ventricular fibrillation (VF) can only be defibrillated after a period of chest compressions and ventilation before the defibrillation attempt. In these patients, unsuccessful defibrillations increase the duration of heart arrest and reduce the possibility of a successful resuscitation, which could be avoided if a reliable prediction for the success of defibrillation could be made. A new method is presented for estimating the irregularity in very short electrocardiographic (ECG) recordings that enables the prediction of a successful defibrillation in patients with VF. This method is based on a recently developed determinism test for very short time series. A slight modification shows that the method can be used to determine relative differences in irregularity of the studied signals. In particular, ECG recordings of VF from patients who could be successfully defibrillated are characterized by a higher level of irregularity, indicating a chaotic nature of the dynamics of the heart, which is in agreement with previous studies on long ECG recordings showing that cardiac chaos was prevalent in healthy heart, whereas in severe congestive heart failure, a decrease in the chaotic behavior was observed.