Assessing the Relationships of Expenditure and Health Outcomes in Healthcare Systems: A System Design Approach

Abstract

Background/Objectives: The COVID-19 pandemic has significantly altered healthcare systems worldwide, highlighting healthcare expenditure's critical role in fostering population resilience and wellness. This extraordinary situation has brought to light the delicate balance that governments must maintain between the need to protect public health and budgetary restraints. The relationship between healthcare expenditure and outcomes, such as healthy life years, health expectancy, and standardized death rate, has become a central point in understanding the dynamics of healthcare systems and their capacity to adapt to emerging challenges. Methods: Using extensive datasets and predictive approaches such as artificial neural networks, exponential smoothing models, and ARIMA techniques, this study explores these connections in the context of the European Union. Results: The study better explains how healthcare financing schemes influence important health outcomes by examining past trends and forecasting future developments. The results show that household healthcare expenditures correlate negatively with standardized death rates and substantially benefit healthy life years and health expectancy. These findings underline the significance of household contributions in influencing health outcomes across various healthcare systems. Long-term and strategic investments in health services are essential, as the pandemic has demonstrated the proactive capacity of well-designed healthcare systems to reduce risks and enhance overall resilience. The results suggest that focused investments can raise life expectancy and lower death rates, supporting the development of robust, adaptable healthcare systems in the post-pandemic era. Conclusions: The main contribution of this research is demonstrating the significant role of healthcare expenditure, particularly household contributions, in improving key health outcomes and fostering healthcare system resilience in the EU context.

Keywords: artificial neural networks; health expectancy; healthcare expenditure; healthcare outcomes; healthcare systems; healthy life years; predictive models; standardized death rate; system design.

Figure 1

Theoretical model. Source: developed by the author. Note: + positive influences; − negative influences.

Figure 2

MLP model. Source: authors’ design using SPSS v27.0 (IBM Corporation, Armonk, NY, USA).

Figure 3

Absolute and normalized importance within the model. Source: authors’ design using SPSS v27.0 (IBM Corporation, Armonk, NY, USA).

Figure 4

SEM model. Source: authors’ design using SmartPLS v3.0 (SmartPLS GmbH, Bönningstedt, Germany).

Figure 5

Forecast of HLYB using the Holt model. Source: author’s design using SPSS v.27 (IBM Corporation, Armonk, NY, USA).

Figure 6

Forecast of HLYB using the Brown model. Source: author’s design using SPSS v.27 (IBM Corporation, Armonk, NY, USA).

Figure 7

The forecast of HCE_AS depends on the previous annual evolution using the ARIMA model. Source: author’s design using SPSS v.27 (IBM Corporation, Armonk, NY, USA).

Figure 8

Forecast of HLYB and HLEB depending on the HCE_AS using the ARIMA model. Source: author’s design using SPSS v.27 (IBM Corporation, Armonk, NY, USA).

Figure 9

Comparison of the forecasting models. Source: author’s design based on data computed using SPSS v.27 (IBM Corporation, Armonk, NY, USA).