Key risk factors associated with fractal dimension based geographical clustering of COVID-19 data in the Flemish and Brussels region, Belgium.

Introduction: COVID-19 remains a major concern globally. Therefore, it is important to evaluate COVID-19's rapidly changing trends. The fractal dimension has been proposed as a viable method to characterize COVID-19 curves since epidemic data is often subject to considerable heterogeneity. In this study, we aim to investigate the association between various socio-demographic factors and the complexity of the COVID-19 curve as quantified through its fractal dimension. Methods: We collected population indicators data (ethnic composition, socioeconomic status, number of inhabitants, population density, the older adult population proportion, vaccination rate, satisfaction, and trust in the government) at the level of the statistical sector in Belgium. We compared these data with fractal dimension indicators of COVID-19 incidence between 1 January - 31 December 2021 using canonical correlation analysis. Results: Our results showed that these population indicators have a significant association with COVID-19 incidences, with the highest explanatory and predictive power coming from the number of inhabitants, population density, and ethnic composition. Conclusion: It is important to monitor these population indicators during a pandemic, especially when dealing with targeted interventions for a specific population.

Predicting key drivers for health care expenditure growth in the Middle East region: a Grossman-PLS modeling approach.

BACKGROUND: Numerous studies have provided evidence to the literature on the demand side of the determinants of health-care expenditure by employing the Grossman model. However, understanding the supply side of the determinants of health-care expenditure will be of crucial importance for improving health outcomes. METHODS: This study has used panel data for 15 Middle-East region countries for the time period of 2000-2016 Initially, Grossman's model of the demand for care is estimated. Furthermore, a Parallel model of the supply of care is estimated for contradistinction analysis. Finally, an integrated partial least square structural equation model is being developed. RESULTS: Results show that the relative wage rate and aging variables are the only indicators that are statistically significant with theoretically consistent signs as postulated by Grossman's theoretical model. The opposite is true with schooling and the proxy of the medical care relative prices. However, in the parallel model, all the four drivers of the demand for care are statistically significant determinants of health-care spending. CONCLUSIONS: Therefore, expansion of health insurance coverage particularly for the elderly cohort of the population could be a promising mechanism to boost the demand for care and eventually improve health outcomes.